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Foraminifera from two cores off eastern Vietnam and the northwestern Philippines, where modern summer and winter monsoon-driven upwelling occurs in the South China Sea, respectively, were analyzed to evaluate the changes in paleoproductivity and upper water structure over the last 220,000 yr. We observed enhanced organic carbon flux and a shoaled thermocline when upwelling intensified off eastern Vietnam during interglacial ages and off the northwestern Philippines during glacial ages. This indicates that the East Asian summer monsoon increased while the winter monsoon decreased during interglacial ages. Particularly, the upwelling reached a maximum off eastern Vietnam during late marine isotopic stage (MIS) 5 and off the northwestern Philippines during MIS 2, implying that the summer monsoon decreased gradually since MIS 5 while the winter monsoon displayed an opposite trend. The variations in upwelling proxies exhibit a distinct cyclicity with frequencies near 41,000 yr and 23,000 yr off eastern Vietnam, in contrast to a strong frequency peak near 100,000 yr off the northwestern Philippines. We suggest that the East Asian summer monsoon has been forced by changes in solar insolation associated with precession and obliquity, while ice-volume forcing is probably a primary factor in determining the strength and timing of the East Asian winter monsoon but with less important insolation forcing.

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Recently, with the development of artificial intelligence technologies and the popularity of mobile devices, walking detection and step counting have gained much attention since they play an important role in the fields of equipment positioning, saving energy, behavior recognition, etc. In this paper, a novel algorithm is proposed to simultaneously detect walking motion and count steps through unconstrained smartphones in the sense that the smartphone placement is not only arbitrary but also alterable. On account of the periodicity of the walking motion and sensitivity of gyroscopes, the proposed algorithm extracts the frequency domain features from three-dimensional (3D) angular velocities of a smartphone through FFT (fast Fourier transform) and identifies whether its holder is walking or not irrespective of its placement. Furthermore, the corresponding step frequency is recursively updated to evaluate the step count in real time. Extensive experiments are conducted by involving eight subjects and different walking scenarios in a realistic environment. It is shown that the proposed method achieves the precision of 93.76% and recall of 93.65% for walking detection, and its overall performance is significantly better than other well-known methods. Moreover, the accuracy of step counting by the proposed method is 95.74%, and is better than both of the several well-known counterparts and commercial products.

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