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  • IOP Publishing  (4)
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  • IOP Publishing  (4)
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  • 1
    Online Resource
    Online Resource
    Research on Operating System Migration Method Based on Domestic Embedded Devices
    IOP Publishing ; 2022
    In:  Journal of Physics: Conference Series Vol. 2171, No. 1 ( 2022-01-01), p. 012067-
    Details
    In: Journal of Physics: Conference Series, IOP Publishing, Vol. 2171, No. 1 ( 2022-01-01), p. 012067-
    Abstract: At present, Google’s Android system has the characteristics of open source, rich software functions, and good user experience, and has been highly recognized by a wide range of applications and the market. If the Loongson platform wants to make a difference in mobile terminals and other fields, it needs to support the Android system. However, although the native Android system supports multiple architectures, it only performs a lot of optimizations for the ARM architecture. For other architectures, Google only provides a generic version. This brings a huge challenge to the Loongson processor based on the MIPS architecture. Based on the analysis of the Android system, this paper studies the Android transplantation method of the Loongson 2K1000 platform in detail realizes the transplantation of the Android Lollipop system on the platform and has passed the verification of the Loongson 2K1000 platform.
    Type of Medium: Online Resource
    ISSN: 1742-6588 , 1742-6596
    URL: Article
    DOI: 10.1088/1742-6596/2171/1/012067
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2022
    detail.hit.zdb_id: 2166409-2
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Discussion on Power System Modeling and Body Optimization of High-speed Electric Vehicle
    IOP Publishing ; 2022
    In:  Journal of Physics: Conference Series Vol. 2200, No. 1 ( 2022-02-01), p. 012004-
    Details
    In: Journal of Physics: Conference Series, IOP Publishing, Vol. 2200, No. 1 ( 2022-02-01), p. 012004-
    Abstract: This paper analyzes the operation mode of high-speed electric vehicle power system, combining with the key points of high-speed electric vehicle power system modeling, including controller (VCU) model, engine model, motor model, power battery model, clutch model, wheel model, power loss model, etc. By studying multi-objective optimization processing, applying genetic algorithm, optimizing variable selection, optimizing problem description, defining constraints, optimizing result analysis, etc., its purpose is to improve the content of high-speed electric vehicle power system and promote the stable development of electric vehicle industry economy.
    Type of Medium: Online Resource
    ISSN: 1742-6588 , 1742-6596
    URL: Article
    DOI: 10.1088/1742-6596/2200/1/012004
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2022
    detail.hit.zdb_id: 2166409-2
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Motor imagery recognition with automatic EEG channel selection and deep learning
    IOP Publishing ; 2020
    In:  Journal of Neural Engineering
    Details
    In: Journal of Neural Engineering, IOP Publishing
    Type of Medium: Online Resource
    ISSN: 1741-2560 , 1741-2552
    URL: Article
    DOI: 10.1088/1741-2552/abca16
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2020
    detail.hit.zdb_id: 2135187-9
    SSG: 12
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  • 4
    Online Resource
    Online Resource
    Compressed sensing of large-scale local field potentials using adaptive sparsity analysis and non-convex optimization
    IOP Publishing ; 2021
    In:  Journal of Neural Engineering Vol. 18, No. 2 ( 2021-04-01), p. 026007-
    Details
    In: Journal of Neural Engineering, IOP Publishing, Vol. 18, No. 2 ( 2021-04-01), p. 026007-
    Abstract: Objective. Energy consumption is a critical issue in resource-constrained wireless neural recording applications with limited data bandwidth. Compressed sensing (CS) has emerged as a powerful framework in addressing this issue owing to its highly efficient data compression procedure. In this paper, a CS-based approach termed simultaneous analysis non-convex optimization (SANCO) is proposed for large-scale, multi-channel local field potentials (LFPs) recording. Approach. The SANCO method consists of three parts: (1) the analysis model is adopted to reinforce sparsity of the multi-channel LFPs, therefore overcoming the drawbacks of conventional synthesis models. (2) An optimal continuous order difference matrix is constructed as the analysis operator, enhancing the recovery performance while saving both computational resources and data storage space. (3) A non-convex optimizer that can by efficiently solved with alternating direction method of multipliers is developed for multi-channel LFPs reconstruction. Main results. Experimental results on real datasets reveal that the proposed approach outperforms state-of-the-art CS methods in terms of both recovery quality and computational efficiency. Significance. Energy efficiency of the SANCO make it an ideal candidate for resource-constrained, large scale wireless neural recording. Particularly, the proposed method ensures that the key features of LFPs had little degradation even when data are compressed by 16x, making it very suitable for long term wireless neural recording applications.
    Type of Medium: Online Resource
    ISSN: 1741-2560 , 1741-2552
    URL: Article
    DOI: 10.1088/1741-2552/abd578
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2021
    detail.hit.zdb_id: 2135187-9
    SSG: 12
    Location Call Number Limitation Availability
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    Online Resource
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