No abstract

In this paper, we propose Auracle, a wearable earpiece that can automatically recognize eating behavior. More specifically, in free-living conditions, we can recognize when and for how long a person is eating. Using an off-the-shelf contact microphone placed behind the ear, Auracle captures the sound of a person chewing as it passes through the bone and tissue of the head. This audio data is then processed by a custom analog/digital circuit board. To ensure reliable (yet comfortable) contact between microphone and skin, all hardware components are incorporated into a 3D-printed behind-the-head framework. We collected field data with 14 participants for 32 hours in free-living conditions and additional eating data with 10 participants for 2 hours in a laboratory setting. We achieved accuracy exceeding 92.8% and F1 score exceeding 77.5% for eating detection. Moreover, Auracle successfully detected 20-24 eating episodes (depending on the metrics) out of 26 in free-living conditions. We demonstrate that our custom device could sense, process, and classify audio data in real time. Additionally, we estimate Auracle can last 28.1 hours with a 110 mAh battery while communicating its observations of eating behavior to a smartphone over Bluetooth.

Abstract:The authors combine the cellular automata with particle system to realize the three-dimensional modeling and visualization of the cloud in the paper. First, we use the principle of particle systems to simulate the outline of the cloud; generate uniform particles in the bounding volumes of the cloud through random function; build the cloud particle system; and initialize the particle number, size, location and related properties. Then the principle of cellular automata system is adopted to deal with uniform particles simulated by the particle system to make it conform to the rules set by the user, and calculate its continuous field density. We render the final cloud particles with a texture map and simulate the more realistic three-dimensional cloud. This method not only obtains the real effect in the simulation, but also improves the rendering performance.

The present study sought to understand the spatiotemporal characteristics, associated with changes in drought disasters during the Ming and Qing Dynasties in North China. The grade sequence of drought disasters at 21 sites for the given period (1470-1912 AD) in North China was studied herein. An ensemble empirical mode decomposition (EEMD) was used to analyze the multiple timescales towards generating a simple and stable intrinsic modal function component. Comparisons and analysis of the frequency and intensity of drought disasters were made using polynomial fitting curves to understand the temporal variations of drought disasters. Two aspects were explored to study the spatial distribution and characteristics of drought disasters. The reconstruction of the sequence of drought disasters was based on the Empirical Orthogonal Function (EOF) and Rotated Empiric Orthogonal Function (REOF). The drought disaster was divided into several space modes and sensitive areas. Drought frequency was recurrent in the northern and low in the southern part of North China. Findings revealed drought frequency and intensity were high in southeast and low in the southwestern part of North China. The study would inform decisions on disaster prevention and mitigation thereby serving as a baseline print for predicting modern drought disasters.