The Experts below are selected from a list of 96 Experts worldwide ranked by ideXlab platform

Hongyi Li - One of the best experts on this subject based on the ideXlab platform.

  • A New Optical Information Processing Device Design for Internet of Brain Things Application
    IEEE Access, 2018
    Co-Authors: Chunli Wang, Yongshun Wang, Hongyi Li
    Abstract:

    With the development and application of Internet of things, new requirements for optical communication technology are put forward, and this text proposed new optical Information Processing Device design logic-oriented-based for Internet of brain things application. Three micro rings were coupled with three different shapes of optical waveguide components, their wavelengths were varied with the change of the applied voltages (logical input) so as to control the propagation direction of the input optical signal and the final output forms of lighting or darkness representing different logic level values as the result of the logical operations. The working wavelength of the Device was determined as $1.5146~\mu \text{m}$ through static simulations with MATLAB tools, and it was proved that the functions of flexible optical switch logic operations “and/not”, “or/nor”, and “xor/xnor” can be realized in the dynamic simulations, the results indicated that the Device with low-power consumption, high-bandwidth, flexible structure, and multiple logical operation functions has been accomplished and can be used as components of VLSI designs in future. This paper also gives a useful reference to many Internet of brain things applied field.

Chunli Wang - One of the best experts on this subject based on the ideXlab platform.

  • A New Optical Information Processing Device Design for Internet of Brain Things Application
    IEEE Access, 2018
    Co-Authors: Chunli Wang, Yongshun Wang, Hongyi Li
    Abstract:

    With the development and application of Internet of things, new requirements for optical communication technology are put forward, and this text proposed new optical Information Processing Device design logic-oriented-based for Internet of brain things application. Three micro rings were coupled with three different shapes of optical waveguide components, their wavelengths were varied with the change of the applied voltages (logical input) so as to control the propagation direction of the input optical signal and the final output forms of lighting or darkness representing different logic level values as the result of the logical operations. The working wavelength of the Device was determined as $1.5146~\mu \text{m}$ through static simulations with MATLAB tools, and it was proved that the functions of flexible optical switch logic operations “and/not”, “or/nor”, and “xor/xnor” can be realized in the dynamic simulations, the results indicated that the Device with low-power consumption, high-bandwidth, flexible structure, and multiple logical operation functions has been accomplished and can be used as components of VLSI designs in future. This paper also gives a useful reference to many Internet of brain things applied field.

Mingjie Huang - One of the best experts on this subject based on the ideXlab platform.

Yongshun Wang - One of the best experts on this subject based on the ideXlab platform.

  • A New Optical Information Processing Device Design for Internet of Brain Things Application
    IEEE Access, 2018
    Co-Authors: Chunli Wang, Yongshun Wang, Hongyi Li
    Abstract:

    With the development and application of Internet of things, new requirements for optical communication technology are put forward, and this text proposed new optical Information Processing Device design logic-oriented-based for Internet of brain things application. Three micro rings were coupled with three different shapes of optical waveguide components, their wavelengths were varied with the change of the applied voltages (logical input) so as to control the propagation direction of the input optical signal and the final output forms of lighting or darkness representing different logic level values as the result of the logical operations. The working wavelength of the Device was determined as $1.5146~\mu \text{m}$ through static simulations with MATLAB tools, and it was proved that the functions of flexible optical switch logic operations “and/not”, “or/nor”, and “xor/xnor” can be realized in the dynamic simulations, the results indicated that the Device with low-power consumption, high-bandwidth, flexible structure, and multiple logical operation functions has been accomplished and can be used as components of VLSI designs in future. This paper also gives a useful reference to many Internet of brain things applied field.

Andrew P Bagshaw - One of the best experts on this subject based on the ideXlab platform.

  • eeg fmri based Information theoretic characterization of the human perceptual decision system
    PLOS ONE, 2012
    Co-Authors: Dirk Ostwald, Camillo Porcaro, Stephen D Mayhew, Andrew P Bagshaw
    Abstract:

    The modern metaphor of the brain is that of a dynamic Information Processing Device. In the current study we investigate how a core cognitive network of the human brain, the perceptual decision system, can be characterized regarding its spatiotemporal representation of task-relevant Information. We capitalize on a recently developed Information theoretic framework for the analysis of simultaneously acquired electroencephalography (EEG) and functional magnetic resonance imaging data (fMRI) (Ostwald et al. (2010), NeuroImage 49: 498–516). We show how this framework naturally extends from previous validations in the sensory to the cognitive domain and how it enables the economic description of neural spatiotemporal Information encoding. Specifically, based on simultaneous EEG-fMRI data features from n = 13 observers performing a visual perceptual decision task, we demonstrate how the Information theoretic framework is able to reproduce earlier findings on the neurobiological underpinnings of perceptual decisions from the response signal features' marginal distributions. Furthermore, using the joint EEG-fMRI feature distribution, we provide novel evidence for a highly distributed and dynamic encoding of task-relevant Information in the human brain.