In order to classify nonlinear features with a linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network（ KPCANet） is proposed. First, the d...
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In order to classify nonlinear features with a linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network（ KPCANet） is proposed. First, the data is mapped into a higher-dimensional space with kernel principal component analysis to make the data linearly separable. Then a two-layer KPCANet is built to obtain the principal components of the image. Finally, the principal components are classified with a linear classifier. Experimental results showthat the proposed KPCANet is effective in face recognition, object recognition and handwritten digit recognition. It also outperforms principal component analysis network（ PCANet） generally. Besides, KPCANet is invariant to illumination and stable to occlusion and slight deformation.
随着传感网络和3G网络的融合,物联网已经成为新世纪最重要的技术之一,如何延长传感节点的工作时间已成为物联网研究的一个重要课题。传统的电源管理规范如APM(Advanced Power Management)和ACPI(Advanced Configuration and Power Inter...
详细信息
随着传感网络和3G网络的融合,物联网已经成为新世纪最重要的技术之一,如何延长传感节点的工作时间已成为物联网研究的一个重要课题。传统的电源管理规范如APM(Advanced Power Management)和ACPI(Advanced Configuration and Power Interface)主要针对PC设计,因其复杂性和对BIOS层要求等因素,在无线传感节点中并不适用。为了解决此问题,针对传感节点计算和存储能力有限的特点,我们首先开发了精简的signalslot框架,基于signal-slot框架,并设计了简单有效的电源管理方案SPM(Simple Power Management),并将SPM在流行的传感节点操作系统Contiki中实现。
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