Sound pressure level spectrum analysis by combination of 4D PTV and ANFIS method around automotive side-view mirror models

This paper proposes a data augmentation method based on artificial intelligence (AI) to obtain sound level spectrum as predicting the spatial and temporal data of time-resolved three-dimensional Particle Tracking Velocimetry (4D PTV) data. A 4D PTV has used to measure flow characteristics of three side mirror models adopting the Shake-The-Box (STB) algorithm with four high-speed cameras on a robotic arm for measuring industrial scale. Helium filled soap bubbles are used as tracers in the wind tunnel experiment to characterize flow structures around automobile side mirror models. Full volumetric velocity fields and evolution of vortex structures are obtained and analyzed. Instantaneous pressure fields are deduced by solving a Poisson equation based on the 4D PTV data. To predict spatial and temporal data of velocity field, artificial intelligence (AI)-based data prediction method has applied. Adaptive Neural Fuzzy Inference System (ANFIS) based machine learning algorithm works well to find 4D missing data behind the automobile side mirror model. Using the ANFIS model, power spectrum of velocity fluctuations and sound level spectrum of pressure fluctuations are successfully obtained to assess flow and noise characteristics of three different side mirror models.

A Novel Subcarrier-Level Spectrum Sensing Method by Utilizing Fine-Grained Channel State Information in Wireless Networks

Traditionally, the channelization structures of wireless technologies (802.11/ZigBee/BLE) have been fixed. Each node content for the spectrum is assigned one channel with a specific bandwidth. However, classical channel-based spectrum sensing and sharing algorithms have great limitations to further optimize spectrum utilization when multiple IoT with different wireless technologies coexisting in the same environment. Therefore, exploring the fine-grained spectrum sensing algorithm becomes an essential work to further improve the spectrum utilization efficiency, especially in the Industrial Scientific Medical (ISM) band. This paper proposes Subcarrier-Sniffer, a novel subcarrier-level spectrum sensing and sharing method, which utilizes channel state information (CSI) to sense the fine-grained status of each subcarrier of the traditional channel. To evaluate the performance of Subcarrier-Sniffer, we implemented Subcarrier-Sniffer by USRP B200min, and the experimental results show that the accuracy of subcarrier-level spectrum sensing could achieve 100% in our settings that the distance between Subcarrier-Sniffer and the monitor is not greater than 7 m. Subcarrier-Sniffer could be applied in WiFi and ZigBee, WiFi and BLE, and WiFi and LTE-U coexisted environments for better spectrum utilization.

Sound Pressure Level Spectra of Automotive Side-View Mirror Models Deduced From Time-Resolved Three-Dimensional Particle Tracking Velocimetry Data With Artificial Intelligence Based Data Assimilation Method

This paper proposes a data assimilation method based on artificial intelligence (AI) to obtain sound level spectrum as increasing the spatial and temporal resolution of time-resolved three-dimensional Particle Tracking Velocimetry (4D PTV) data. A 4D PTV has used to measure flow characteristics of three side mirror models adopting the Shake-The-Box (STB) algorithm with four high-speed cameras on a robotic arm for measuring industrial scale. Helium filled soap bubbles are used as tracers in the wind tunnel experiment to characterize flow structures around automobile side mirror models. Full volumetric velocity fields and evolution of vortex structures are obtained and analyzed. Instantaneous pressure fields are deduced by solving a Poisson equation based on the 4D PTV data. To increase spatial and temporal resolutions of velocity field, artificial intelligence (AI)-based data assimilation method has applied. Adaptive Neural Fuzzy Inference System (ANFIS) based machine learning algorithm works well to find hidden 3D vortical structures behind the automobile side mirror model. Using the high resolution ANFIS model, power spectrum of velocity fluctuations and sound level spectrum of pressure fluctuations are successfully obtained to assess flow and noise characteristics of three different side mirror models.

Subcarrier-Sniffer：CSI-based Subcarrier-Level Spectrum Sensing Algorithm

As the massive deployment of the heterogeneous IoT devices in the coexisting environment such as smart homes，Traditional channel-based spectrum sharing algorithms such as CSMA has great limitations to further optimize spectrum utilization. Therefore, exploring more efficient spectrum sensing algorithm becomes hot topic these years. This paper proposes Subcarrier-Sniffer, which utilizes Channel State Information (CSI) to sense the subcarrier-level detailed status of the spectrum. In order to evaluate the performance of Subcarrier-Sniffer, we implemented Subcarrier-Sniffer by USRP B200min, and the experimental results show that when the distance between Subcarrier-Sniffer and the monitored devices is not great than 7 m, the accuracy of subcarrier-level spectrum sensing could achieve 100% in our settings.

Nocturnal Melatonin Suppression by Adolescents and Adults for Different Levels, Spectra, and Durations of Light Exposure

The human circadian system is primarily regulated by the 24-h LD cycle incident on the retina, and nocturnal melatonin suppression is a primary outcome measure for characterizing the biological clock’s response to those light exposures. A limited amount of data related to the combined effects of light level, spectrum, and exposure duration on nocturnal melatonin suppression has impeded the development of circadian-effective lighting recommendations and light-treatment methods. The study’s primary goal was to measure nocturnal melatonin suppression for a wide range of light levels (40 to 1000 lux), 2 white light spectra (2700 K and 6500 K), and an extended range of nighttime light exposure durations (0.5 to 3.0 h). The study’s second purpose was to examine whether differences existed between adolescents’ and adults’ circadian sensitivity to these lighting characteristics. The third purpose was to provide an estimate of the absolute threshold for the impact of light on acute melatonin suppression. Eighteen adolescents (age range, 13 to 18 years) and 23 adults (age range, 24 to 55 years) participated in the study. Results showed significant main effects of light level, spectrum, and exposure duration on melatonin suppression. Moreover, the data also showed that the relative suppressing effect of light on melatonin diminishes with increasing exposure duration for both age groups and both spectra. The present results do not corroborate our hypothesis that adolescents exhibit greater circadian sensitivity to short-wavelength radiation compared with adults. As for threshold, it takes longer to observe significant melatonin suppression at lower CS levels than at higher CS levels. Dose-response curves (amount and duration) for both white-light spectra and both age groups can guide lighting recommendations when considering circadian-effective light in applications such as offices, schools, residences, and healthcare facilities.