The Experts below are selected from a list of 349476 Experts worldwide ranked by ideXlab platform
Pedro A Valdessosa - One of the best experts on this subject based on the ideXlab platform.
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issues and recommendations from the ohbm cobidas meeg committee for reproducible eeg and meg research
Nature Neuroscience, 2020Co-Authors: Cyril Pernet, Marta I Garrido, Alexandre Gramfort, Natasha M Maurits, Christoph M Michel, Elizabeth Pang, Riitta Salmelin, Janmathijs Schoffelen, Pedro A ValdessosaAbstract:The Organization for Human Brain Mapping (OHBM) has been active in advocating for the instantiation of best practices in neuroimaging data Acquisition, Analysis, reporting and sharing of both data and Analysis code to deal with issues in science related to reproducibility and replicability. Here we summarize recommendations for such practices in magnetoencephalographic (MEG) and electroencephalographic (EEG) research, recently developed by the OHBM neuroimaging community known by the abbreviated name of COBIDAS MEEG. We discuss the rationale for the guidelines and their general content, which encompass many topics under active discussion in the field. We highlight future opportunities and challenges to maximizing the sharing and exploitation of MEG and EEG data, and we also discuss how this 'living' set of guidelines will evolve to continually address new developments in neurophysiological assessment methods and multimodal integration of neurophysiological data with other data types.
Herbert Voigt - One of the best experts on this subject based on the ideXlab platform.
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a two channel action potential generator for testing neurophysiologic data Acquisition Analysis systems
Journal of Neuroscience Methods, 1995Co-Authors: Ronald S Lisiecki, Herbert VoigtAbstract:A 2-channel action-potential generator system was designed for use in testing neurophysiologic data Acquisition/Analysis systems. The system consists of a personal computer controlling an external hardware unit. This system is capable of generating 2 channels of simulated action potential (AP) waveshapes. The AP waveforms are generated from the linear combination of 2 principal-component template functions. Each channel generates randomly occurring APs with a specified rate ranging from 1 to 200 events per second. The 2 trains may be independent of one another or the second channel may be made to be excited or inhibited by the events from the first channel with user-specified probabilities. A third internal channel may be made to excite or inhibit events in both of the 2 output channels with user-specified rate parameters and probabilities. The system produces voltage waveforms that may be used to test neurophysiologic data Acquisition systems for recording from 2 spike trains simultaneously and for testing multispike-train Analysis (e.g., cross-correlation) software.
Yoon Joo-sung - One of the best experts on this subject based on the ideXlab platform.
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Smart Factory Information Service Bus(SIBUS) for Manufacturing Application: Requirement, Architecture and Implementation
'Springer Science and Business Media LLC', 2018Co-Authors: Yoon S, Um J, Suh S.-h, Stroud Ian, Yoon Joo-sungAbstract:The Smart Factory is an important topic worldwide as a means for achieving Industry 4.0 in the manufacturing domain. Contemporary research on the Smart Factory has been concerned with application of the so-called Internet of Things (IoT) to the shop floor. However, IoT in this context is often restricted to solving local problems such as managing product information, collaborative information exchange, and increasing productivity. To take full advantage of the potential of the IoT in manufacturing systems, it is necessary that the information service perspective should receive keen attention. This paper proposes a reference architecture for the information service bus or middleware for the Smart Factory that can be used for information Acquisition, Analysis, and application for the various stakeholders at the levels of Machine, Factory, and Enterprise Resource Planning. To reflect the real voice of the industry, real industrial problems have been identified, transformed into requirements, and incorporated into the information architecture; i.e., Smart Factory Information Service Bus. The implementation process of the reference architecture is also presented and illustrated via case studies.The Smart Factory is an important topic worldwide as a means for achieving Industry 4.0 in the manufacturing domain. Contemporary research on the Smart Factory has been concerned with application of the so-called Internet of Things (IoT) to the shop floor. However, IoT in this context is often restricted to solving local problems such as managing product information, collaborative information exchange, and increasing productivity. To take full advantage of the potential of the IoT in manufacturing systems, it is necessary that the information service perspective should receive keen attention. This paper proposes a reference architecture for the information service bus or middleware for the Smart Factory that can be used for information Acquisition, Analysis, and application for the various stakeholders at the levels of Machine, Factory, and Enterprise Resource Planning. To reflect the real voice of the industry, real industrial problems have been identified, transformed into requirements, and incorporated into the information architecture; i.e., Smart Factory Information Service Bus. The implementation process of the reference architecture is also presented and illustrated via case studies.1
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Smart Factory Information Service Bus(SIBUS) for Manufacturing Application: Requirement, Architecture and Implementation
'Springer Science and Business Media LLC', 2018Co-Authors: Yoon S, Um J, Suh S.-h, Stroud Ian, Yoon Joo-sungAbstract:The Smart Factory is an important topic worldwide as a means for achieving Industry 4.0 in the manufacturing domain. Contemporary research on the Smart Factory has been concerned with application of the so-called Internet of Things (IoT) to the shop floor. However, IoT in this context is often restricted to solving local problems such as managing product information, collaborative information exchange, and increasing productivity. To take full advantage of the potential of the IoT in manufacturing systems, it is necessary that the information service perspective should receive keen attention. This paper proposes a reference architecture for the information service bus or middleware for the Smart Factory that can be used for information Acquisition, Analysis, and application for the various stakeholders at the levels of Machine, Factory, and Enterprise Resource Planning. To reflect the real voice of the industry, real industrial problems have been identified, transformed into requirements, and incorporated into the information architecture; i.e., Smart Factory Information Service Bus. The implementation process of the reference architecture is also presented and illustrated via case studies.112sciescopu
T S Wey - One of the best experts on this subject based on the ideXlab platform.
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the development of multi channel action potential generator for testing neurophysiologic data Acquisition Analysis system
IEEE International Workshop on Biomedical Circuits and Systems, 2004Co-Authors: Y H Sheu, C W Chen, C H Chen, T S WeyAbstract:In our research, the multi-channel action-potentials (APs) generator based on high-speed USB interface is designed to test neurophysiologic data Acquisition/Analysis system. This multi-channel APs generator is composed of a personal computer and an external hardware unit. This generator is not only capable of generating a simulated APs waveform, and also generating random occurred APs with a specified rate ranging form 1 to 200 events per second. The waveform of each channel can be easily changed by using 2 parameters. Additionally, the programmable system on a Chip (PSoC/sup /spl trade//) component is adopted to simplify circuit units. Specifically, 16 channels, the maximum of the functions in this generator, can be spontaneously reached. Moreover, an application software is developed to generate APs waveform and a digital oscilloscope is used to verify the waveform as well. The voltage waveform produced by APs generator can be used to test neurophysiologic data Acquisition systems for recording a spike trains and testing multispike-train Analysis.
Davide Ragazzi - One of the best experts on this subject based on the ideXlab platform.
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Interactive Data Exploration as a Service for the Smart Factory
2017 IEEE International Conference on Web Services (ICWS), 2017Co-Authors: Ada Bagozi, Devis Bianchini, Valeria De Antonellis, Alessandro Marini, Davide RagazziAbstract:In the era of Internet of Things and dynamically interconnected systems, real time data becomes a new industrial asset, used to create new opportunities for operations improvement and to increase industrial value through the capitalisation of immaterial assets. In the smart factory, big data Acquisition, Analysis and visualisation pave the way to the manufacturing servitization, defined as the strategic innovation of organisations capabilities and processes to shift from product offering to an integrated "product plus service" offering. According to this vision, interconnected physical systems are associated with a cyber twin, where innovative services for big data management should be provided. In this paper, we propose an interactive data exploration framework, that poses a service-oriented perspective on the smart factory. Large amounts of data are incrementally collected from physical systems, organized and analysed on the cloud and new services are provided to enable data exploration. Such services implement novel data summarisation techniques, based on clustering, to manage data abundance, and data relevance evaluation techniques, aimed to focus the attention on relevant data that is being explored. Services are based on a multi-dimensional model, that is suited for supporting the iterative and multi-step exploration of Big Data.
