The Experts below are selected from a list of 77634 Experts worldwide ranked by ideXlab platform

Em Drakakis - One of the best experts on this subject based on the ideXlab platform.

  • traumatic brain injury neuroelectrochemical monitoring behind the ear micro instrument and Cloud Application
    Journal of Neuroengineering and Rehabilitation, 2020
    Co-Authors: Mk Tageldeen, Mg Boutelle, Sally A N Gowers, Chi L Leong, Em Drakakis
    Abstract:

    Traumatic Brain Injury (TBI) is a leading cause of fatality and disability worldwide, partly due to the occurrence of secondary injury and late interventions. Correct diagnosis and timely monitoring ensure effective medical intervention aimed at improving clinical outcome. However, due to the limitations in size and cost of current ambulatory bioinstruments, they cannot be used to monitor patients who may still be at risk of secondary injury outside the ICU. We propose a complete system consisting of a wearable wireless bioinstrument and a Cloud-based Application for real-time TBI monitoring. The bioinstrument can simultaneously record up to ten channels including both ECoG biopotential and neurochemicals (e.g. potassium, glucose and lactate), and supports various electrochemical methods including potentiometry, amperometry and cyclic voltammetry. All channels support variable gain programming to automatically tune the input dynamic range and address biosensors’ falling sensitivity. The instrument is flexible and can be folded to occupy a small space behind the ear. A Bluetooth Low-Energy (BLE) receiver is used to wirelessly connect the instrument to a Cloud Application where the recorded data is stored, processed and visualised in real-time. Bench testing has been used to validate device performance. The instrument successfully monitored spreading depolarisations (SDs) - reproduced using a signal generator - with an SNR of 29.07 dB and NF of 0.26 dB. The potentiostat generates a wide voltage range from -1.65V to +1.65V with a resolution of 0.8mV and the sensitivity of the amperometric AFE was verified by recording 5 pA currents. Different potassium, glucose and lactate concentrations prepared in lab were accurately measured and their respective working curves were constructed. Finally,the instrument achieved a maximum sampling rate of 1.25 ksps/channel with a throughput of 105 kbps. All measurements were successfully received at the Cloud. The proposed instrument uniquely positions itself by presenting an aggressive optimisation of size and cost while maintaining high measurement accuracy. The system can effectively extend neuroelectrochemical monitoring to all TBI patients including those who are mobile and those who are outside the ICU. Finally, data recorded in the Cloud Application could be used to help diagnosis and guide rehabilitation.

  • Traumatic brain injury neuroelectrochemical monitoring: behind-the-ear micro-instrument and Cloud Application
    'Springer Science and Business Media LLC', 2020
    Co-Authors: Mk Tageldeen, Gowers San, Cl Leong, Mg Boutelle, Em Drakakis
    Abstract:

    BACKGROUND: Traumatic Brain Injury (TBI) is a leading cause of fatality and disability worldwide, partly due to the occurrence of secondary injury and late interventions. Correct diagnosis and timely monitoring ensure effective medical intervention aimed at improving clinical outcome. However, due to the limitations in size and cost of current ambulatory bioinstruments, they cannot be used to monitor patients who may still be at risk of secondary injury outside the ICU. METHODS: We propose a complete system consisting of a wearable wireless bioinstrument and a Cloud-based Application for real-time TBI monitoring. The bioinstrument can simultaneously record up to ten channels including both ECoG biopotential and neurochemicals (e.g. potassium, glucose and lactate), and supports various electrochemical methods including potentiometry, amperometry and cyclic voltammetry. All channels support variable gain programming to automatically tune the input dynamic range and address biosensors' falling sensitivity. The instrument is flexible and can be folded to occupy a small space behind the ear. A Bluetooth Low-Energy (BLE) receiver is used to wirelessly connect the instrument to a Cloud Application where the recorded data is stored, processed and visualised in real-time. Bench testing has been used to validate device performance. RESULTS: The instrument successfully monitored spreading depolarisations (SDs) - reproduced using a signal generator - with an SNR of 29.07 dB and NF of 0.26 dB. The potentiostat generates a wide voltage range from -1.65V to +1.65V with a resolution of 0.8mV and the sensitivity of the amperometric AFE was verified by recording 5 pA currents. Different potassium, glucose and lactate concentrations prepared in lab were accurately measured and their respective working curves were constructed. Finally,the instrument achieved a maximum sampling rate of 1.25 ksps/channel with a throughput of 105 kbps. All measurements were successfully received at the Cloud. CONCLUSION: The proposed instrument uniquely positions itself by presenting an aggressive optimisation of size and cost while maintaining high measurement accuracy. The system can effectively extend neuroelectrochemical monitoring to all TBI patients including those who are mobile and those who are outside the ICU. Finally, data recorded in the Cloud Application could be used to help diagnosis and guide rehabilitation

Frank Leymann - One of the best experts on this subject based on the ideXlab platform.

  • from pattern languages to solution implementations
    PATTERNS 2014 The Sixth International Conferences on Pervasive Patterns and Applications, 2014
    Co-Authors: Michael Falkenthal, Uwe Breitenbücher, Johanna Barzen, Christoph Fehling, Frank Leymann
    Abstract:

    Patterns are a well-known and often used concept in the domain of computer science. They document proven solutions to recurring problems in a specific context and in a generic way. So patterns are applicable in a multiplicity of specific use cases. However, since the concept of patterns aims at generalization and abstraction of solution knowledge, it is difficult to apply solutions provided by patterns to specific use cases, as the required knowledge about refinement and the manual effort that has to be spent is immense. Therefore, we introduce the concept of Solution Implementations, which are directly associated to patterns to efficiently support elaboration of concrete pattern implementations. We show how Solution Implementations can be aggregated to solve problems that require the Application of multiple patterns at once. We validate the presented approach in the domain of Cloud Application architecture and Cloud Application management and show the feasibility of our approach with a prototype. Keywords-pattern; pattern languages; pattern-based solution; pattern Application; Cloud computing patterns

  • combining declarative and imperative Cloud Application provisioning based on tosca
    IEEE International Conference on Cloud Engineering, 2014
    Co-Authors: Uwe Breitenbücher, Kálmán Képes, Tobias Binz, Oliver Kopp, Frank Leymann, Johannes Wettinger
    Abstract:

    The automation of Application provisioning is one of the most important issues in Cloud Computing. The Topology and Orchestration Specification for Cloud Applications (TOSCA) supports automating provisioning by two different flavors: (i) declarative processing is based on interpreting Application topology models by a runtime that infers provisioning logic whereas (ii) imperative processing employs provisioning plans that explicitly describe the provisioning tasks to be executed. Both flavors come with benefits and drawbacks. This paper presents a means to combine both flavors to resolve drawbacks and to profit from benefits of both worlds: we propose a standards-based approach to generate provisioning plans based on TOSCA topology models. These provisioning plans are workflows that can be executed fully automatically and may be customized by Application developers after generation. We prove the technical feasibility of the approach by an end-to-end open source toolchain and evaluate its extensibility, performance, and complexity.

  • automating Cloud Application management using management idioms
    2014
    Co-Authors: Uwe Breitenbücher, Tobias Binz, Oliver Kopp, Frank Leymann
    Abstract:

    Patterns are a well-established concept to document generic solutions for recurring problems in an abstract manner. Especially in Information Technology (IT), many pattern languages exist that ease creating Application architectures, designs, and management processes. Their generic nature provides a powerful means to describe knowledge in an abstract fashion that can be reused and refined for concrete use cases. However, the required manual refinement currently prevents applying the concept of patterns efficiently in the domain of Cloud Application Management as automation is one of the most important requirements in Cloud Computing. This paper presents an approach that enables automating both (i) the refinement of management patterns for individual use cases and (ii) the execution of the refined solutions: we introduce Automated Management Idioms to refine patterns automatically and extend an existing management framework to generate executable management workflows based on these refinements. We validate the presented approach by a prototypical implementation to prove its technical feasibility and evaluate its extensibility, standards compliance, and complexity. Keywords—Application Management; Automation; Patterns; Idioms; Cloud Computing

  • integrated Cloud Application provisioning interconnecting service centric and script centric management technologies
    OTM Confederated International Conferences "On the Move to Meaningful Internet Systems", 2013
    Co-Authors: Uwe Breitenbücher, Oliver Kopp, Tobias Binz, Frank Leymann, Johannes Wettinger
    Abstract:

    Modern Cloud Applications employ a plethora of components and XaaS offerings that need to be configured during provisioning. Due to increased heterogeneity, complexity is growing and existing approaches reach their limits if multiple different provisioning and configuration technologies are involved. They are not able to integrate them in an automated, flexible, and customizable way. Especially combining proprietary management services with script-centric configuration management technologies is currently a major challenge. To enable automated provisioning of such Applications, we introduce Generic Lifecycle Management Planlets that provide a means to combine custom provisioning logic with common provisioning tasks. We implemented planlets for provisioning and customization of components and XaaS offerings based on both SOAP and RESTful Web services as well as configuration management technologies such as Chef to show the feasibility of the approach. By using our approach, multiple technologies can be combined seamlessly.

  • bpmn4tosca a domain specific language to model management plans for composite Applications
    Business Process Modeling Notation, 2012
    Co-Authors: Oliver Kopp, Tobias Binz, Uwe Breitenbücher, Frank Leymann
    Abstract:

    TOSCA is an upcoming standard to capture Cloud Application topologies and their management in a portable way. Management aspects include provisioning, operation and deprovisioning of an Application. Management plans capture these aspects in workflows. BPMN 2.0 as general-purpose language can be used to model these workflows. There is, however, no tailored support for management plans in BPMN. This paper analyzes TOSCA with the focus on requirements on workflow modeling languages to come up with a strong link to the Application topology with the goal to improve modeling support. To simplify the modeling of management plans, we introduce BPMN4TOSCA, which extends BPMN with four TOSCA-specific elements: TOSCA Topology Management Task, TOSCA Node Management Task, TOSCA Script Task, and TOSCA Data Object. Portability is ensured by a transformation of BPMN4TOSCA to plain BPMN. A prototypical modeling tool supports the strong link between the management plan and the TOSCA topology.

Mk Tageldeen - One of the best experts on this subject based on the ideXlab platform.

  • traumatic brain injury neuroelectrochemical monitoring behind the ear micro instrument and Cloud Application
    Journal of Neuroengineering and Rehabilitation, 2020
    Co-Authors: Mk Tageldeen, Mg Boutelle, Sally A N Gowers, Chi L Leong, Em Drakakis
    Abstract:

    Traumatic Brain Injury (TBI) is a leading cause of fatality and disability worldwide, partly due to the occurrence of secondary injury and late interventions. Correct diagnosis and timely monitoring ensure effective medical intervention aimed at improving clinical outcome. However, due to the limitations in size and cost of current ambulatory bioinstruments, they cannot be used to monitor patients who may still be at risk of secondary injury outside the ICU. We propose a complete system consisting of a wearable wireless bioinstrument and a Cloud-based Application for real-time TBI monitoring. The bioinstrument can simultaneously record up to ten channels including both ECoG biopotential and neurochemicals (e.g. potassium, glucose and lactate), and supports various electrochemical methods including potentiometry, amperometry and cyclic voltammetry. All channels support variable gain programming to automatically tune the input dynamic range and address biosensors’ falling sensitivity. The instrument is flexible and can be folded to occupy a small space behind the ear. A Bluetooth Low-Energy (BLE) receiver is used to wirelessly connect the instrument to a Cloud Application where the recorded data is stored, processed and visualised in real-time. Bench testing has been used to validate device performance. The instrument successfully monitored spreading depolarisations (SDs) - reproduced using a signal generator - with an SNR of 29.07 dB and NF of 0.26 dB. The potentiostat generates a wide voltage range from -1.65V to +1.65V with a resolution of 0.8mV and the sensitivity of the amperometric AFE was verified by recording 5 pA currents. Different potassium, glucose and lactate concentrations prepared in lab were accurately measured and their respective working curves were constructed. Finally,the instrument achieved a maximum sampling rate of 1.25 ksps/channel with a throughput of 105 kbps. All measurements were successfully received at the Cloud. The proposed instrument uniquely positions itself by presenting an aggressive optimisation of size and cost while maintaining high measurement accuracy. The system can effectively extend neuroelectrochemical monitoring to all TBI patients including those who are mobile and those who are outside the ICU. Finally, data recorded in the Cloud Application could be used to help diagnosis and guide rehabilitation.

  • Traumatic brain injury neuroelectrochemical monitoring: behind-the-ear micro-instrument and Cloud Application
    'Springer Science and Business Media LLC', 2020
    Co-Authors: Mk Tageldeen, Gowers San, Cl Leong, Mg Boutelle, Em Drakakis
    Abstract:

    BACKGROUND: Traumatic Brain Injury (TBI) is a leading cause of fatality and disability worldwide, partly due to the occurrence of secondary injury and late interventions. Correct diagnosis and timely monitoring ensure effective medical intervention aimed at improving clinical outcome. However, due to the limitations in size and cost of current ambulatory bioinstruments, they cannot be used to monitor patients who may still be at risk of secondary injury outside the ICU. METHODS: We propose a complete system consisting of a wearable wireless bioinstrument and a Cloud-based Application for real-time TBI monitoring. The bioinstrument can simultaneously record up to ten channels including both ECoG biopotential and neurochemicals (e.g. potassium, glucose and lactate), and supports various electrochemical methods including potentiometry, amperometry and cyclic voltammetry. All channels support variable gain programming to automatically tune the input dynamic range and address biosensors' falling sensitivity. The instrument is flexible and can be folded to occupy a small space behind the ear. A Bluetooth Low-Energy (BLE) receiver is used to wirelessly connect the instrument to a Cloud Application where the recorded data is stored, processed and visualised in real-time. Bench testing has been used to validate device performance. RESULTS: The instrument successfully monitored spreading depolarisations (SDs) - reproduced using a signal generator - with an SNR of 29.07 dB and NF of 0.26 dB. The potentiostat generates a wide voltage range from -1.65V to +1.65V with a resolution of 0.8mV and the sensitivity of the amperometric AFE was verified by recording 5 pA currents. Different potassium, glucose and lactate concentrations prepared in lab were accurately measured and their respective working curves were constructed. Finally,the instrument achieved a maximum sampling rate of 1.25 ksps/channel with a throughput of 105 kbps. All measurements were successfully received at the Cloud. CONCLUSION: The proposed instrument uniquely positions itself by presenting an aggressive optimisation of size and cost while maintaining high measurement accuracy. The system can effectively extend neuroelectrochemical monitoring to all TBI patients including those who are mobile and those who are outside the ICU. Finally, data recorded in the Cloud Application could be used to help diagnosis and guide rehabilitation

Rajkumar Buyya - One of the best experts on this subject based on the ideXlab platform.

  • deadline driven provisioning of resources for scientific Applications in hybrid Clouds with aneka
    Future Generation Computer Systems, 2012
    Co-Authors: Christian Vecchiola, Dileban Karunamoorthy, Rodrigo N Calheiros, Rajkumar Buyya
    Abstract:

    Scientific Applications require large computing power, traditionally exceeding the amount that is available within the premises of a single institution. Therefore, Clouds can be used to provide extra resources whenever required. For this vision to be achieved, however, requires both policies defining when and how Cloud resources are allocated to Applications and a platform implementing not only these policies but also the whole software stack supporting management of Applications and resources. Aneka is a Cloud Application platform capable of provisioning resources obtained from a variety of sources, including private and public Clouds, clusters, grids, and desktops grids. In this paper, we present Aneka's deadline-driven provisioning mechanism, which is responsible for supporting quality of service (QoS)-aware execution of scientific Applications in hybrid Clouds composed of resources obtained from a variety of sources. Experimental results evaluating such a mechanism show that Aneka is able to efficiently allocate resources from different sources in order to reduce Application execution times.

  • aneka Cloud Application platform and its integration with windows azure
    arXiv: Distributed Parallel and Cluster Computing, 2011
    Co-Authors: Karthik Sukumar, Christian Vecchiola, Dileban Karunamoorthy, Rajkumar Buyya
    Abstract:

    Aneka is an Application Platform-as-a-Service (Aneka PaaS) for Cloud Computing. It acts as a framework for building customized Applications and deploying them on either public or private Clouds. One of the key features of Aneka is its support for provisioning resources on different public Cloud providers such as Amazon EC2, Windows Azure and GoGrid. In this chapter, we will present Aneka platform and its integration with one of the public Cloud infrastructures, Windows Azure, which enables the usage of Windows Azure Compute Service as a resource provider of Aneka PaaS. The integration of the two platforms will allow users to leverage the power of Windows Azure Platform for Aneka Cloud Computing, employing a large number of compute instances to run their Applications in parallel. Furthermore, customers of the Windows Azure platform can benefit from the integration with Aneka PaaS by embracing the advanced features of Aneka in terms of multiple programming models, scheduling and management services, Application execution services, accounting and pricing services and dynamic provisioning services. Finally, in addition to the Windows Azure Platform we will illustrate in this chapter the integration of Aneka PaaS with other public Cloud platforms such as Amazon EC2 and GoGrid, and virtual machine management platforms such as Xen Server. The new support of provisioning resources on Windows Azure once again proves the adaptability, extensibility and flexibility of Aneka.

Mg Boutelle - One of the best experts on this subject based on the ideXlab platform.

  • traumatic brain injury neuroelectrochemical monitoring behind the ear micro instrument and Cloud Application
    Journal of Neuroengineering and Rehabilitation, 2020
    Co-Authors: Mk Tageldeen, Mg Boutelle, Sally A N Gowers, Chi L Leong, Em Drakakis
    Abstract:

    Traumatic Brain Injury (TBI) is a leading cause of fatality and disability worldwide, partly due to the occurrence of secondary injury and late interventions. Correct diagnosis and timely monitoring ensure effective medical intervention aimed at improving clinical outcome. However, due to the limitations in size and cost of current ambulatory bioinstruments, they cannot be used to monitor patients who may still be at risk of secondary injury outside the ICU. We propose a complete system consisting of a wearable wireless bioinstrument and a Cloud-based Application for real-time TBI monitoring. The bioinstrument can simultaneously record up to ten channels including both ECoG biopotential and neurochemicals (e.g. potassium, glucose and lactate), and supports various electrochemical methods including potentiometry, amperometry and cyclic voltammetry. All channels support variable gain programming to automatically tune the input dynamic range and address biosensors’ falling sensitivity. The instrument is flexible and can be folded to occupy a small space behind the ear. A Bluetooth Low-Energy (BLE) receiver is used to wirelessly connect the instrument to a Cloud Application where the recorded data is stored, processed and visualised in real-time. Bench testing has been used to validate device performance. The instrument successfully monitored spreading depolarisations (SDs) - reproduced using a signal generator - with an SNR of 29.07 dB and NF of 0.26 dB. The potentiostat generates a wide voltage range from -1.65V to +1.65V with a resolution of 0.8mV and the sensitivity of the amperometric AFE was verified by recording 5 pA currents. Different potassium, glucose and lactate concentrations prepared in lab were accurately measured and their respective working curves were constructed. Finally,the instrument achieved a maximum sampling rate of 1.25 ksps/channel with a throughput of 105 kbps. All measurements were successfully received at the Cloud. The proposed instrument uniquely positions itself by presenting an aggressive optimisation of size and cost while maintaining high measurement accuracy. The system can effectively extend neuroelectrochemical monitoring to all TBI patients including those who are mobile and those who are outside the ICU. Finally, data recorded in the Cloud Application could be used to help diagnosis and guide rehabilitation.

  • Traumatic brain injury neuroelectrochemical monitoring: behind-the-ear micro-instrument and Cloud Application
    'Springer Science and Business Media LLC', 2020
    Co-Authors: Mk Tageldeen, Gowers San, Cl Leong, Mg Boutelle, Em Drakakis
    Abstract:

    BACKGROUND: Traumatic Brain Injury (TBI) is a leading cause of fatality and disability worldwide, partly due to the occurrence of secondary injury and late interventions. Correct diagnosis and timely monitoring ensure effective medical intervention aimed at improving clinical outcome. However, due to the limitations in size and cost of current ambulatory bioinstruments, they cannot be used to monitor patients who may still be at risk of secondary injury outside the ICU. METHODS: We propose a complete system consisting of a wearable wireless bioinstrument and a Cloud-based Application for real-time TBI monitoring. The bioinstrument can simultaneously record up to ten channels including both ECoG biopotential and neurochemicals (e.g. potassium, glucose and lactate), and supports various electrochemical methods including potentiometry, amperometry and cyclic voltammetry. All channels support variable gain programming to automatically tune the input dynamic range and address biosensors' falling sensitivity. The instrument is flexible and can be folded to occupy a small space behind the ear. A Bluetooth Low-Energy (BLE) receiver is used to wirelessly connect the instrument to a Cloud Application where the recorded data is stored, processed and visualised in real-time. Bench testing has been used to validate device performance. RESULTS: The instrument successfully monitored spreading depolarisations (SDs) - reproduced using a signal generator - with an SNR of 29.07 dB and NF of 0.26 dB. The potentiostat generates a wide voltage range from -1.65V to +1.65V with a resolution of 0.8mV and the sensitivity of the amperometric AFE was verified by recording 5 pA currents. Different potassium, glucose and lactate concentrations prepared in lab were accurately measured and their respective working curves were constructed. Finally,the instrument achieved a maximum sampling rate of 1.25 ksps/channel with a throughput of 105 kbps. All measurements were successfully received at the Cloud. CONCLUSION: The proposed instrument uniquely positions itself by presenting an aggressive optimisation of size and cost while maintaining high measurement accuracy. The system can effectively extend neuroelectrochemical monitoring to all TBI patients including those who are mobile and those who are outside the ICU. Finally, data recorded in the Cloud Application could be used to help diagnosis and guide rehabilitation