Performance Analysis of Three-Wavelength Multi-Channel Photonic Crystal Filters of Different Sizes

Multi-wavelength and multi-channel photonic crystal filters are designed with different sizes considered by using a two-dimensional quadric lattice photonic crystal structure to solve the problems of a multi-channel filter with structure complexity, single-wavelength download, and channel interference. The designed filter consists of a waveguide, reflection wall, multimode microcavity, and output port. Each port can download three different wavelengths. In the communication band from 1.500 to 1.600 μm, the transmittance of each channel is greater than 90%, and the filtering efficiency is high. The size of the non-simplified filter is only 27 μm × 17 μm. On the premise of ensuring low loss transmittance (that is, the transmittance of each port is changed by no more than 10% at the wavelength from 1.5–1.6 μm), the size of the filter can reach 15 μm × 7 μm. This design will greatly reduce the overall structure size of the filter and is suitable for multiplexing and demultiplexing in WDM systems.

The Mars system revealed by the Martian Moons eXploration mission

Japan Aerospace Exploration Agency (JAXA) plans a Phobos sample return mission (MMX: Martian Moons eXploration). In this study, we review the related works on the past climate of Mars, its evolution, and the present climate and weather to describe the scientific goals and strategies of the MMX mission regarding the evolution of the Martian surface environment. The MMX spacecraft will retrieve and return a sample of Phobos regolith back to Earth in 2029. Mars ejecta are expected to be accumulated on the surface of Phobos without being much shocked. Samples from Phobos probably contain all types of Martian rock from sedimentary to igneous covering all geological eras if ejecta from Mars could be accumulated on the Phobos surface. Therefore, the history of the surface environment of Mars can be restored by analyzing the returned samples. Remote sensing of the Martian atmosphere and monitoring ions escaping to space while the spacecraft is orbiting Mars in the equatorial orbit are also planned. The camera with multi-wavelength filters and the infrared spectrometer onboard the spacecraft can monitor rapid transport processes of water vapor, dust, ice clouds, and other species, which could not be traced by the previous satellites on the sun-synchronous polar orbit. Such time-resolved pictures of the atmospheric phenomena should be an important clue to understand both the processes of water exchange between the surface/underground reservoirs and the atmosphere and the drivers of efficient material transport to the upper atmosphere. The mass spectrometer with unprecedented mass resolution can observe ions escaping to space and monitor the atmospheric escape which has made the past Mars to evolve towards the cold and dry surface environment we know today. Together with the above two instruments, it can potentially reveal what kinds of atmospheric events can transport tracers (e.g., H2O) upward and enhance the atmospheric escape. Graphical Abstract

On the association between aerosol optical depths and surface meteorological conditions in a tropical coastal environment

Columnar aerosol spectral optical depth data, estimated using a ground based passive multi-wavelength solar radiometer at the tropical coastal station of Thumba, Thiruvananthapuram (Trivandrum) (8.55°N, 77°E) during the period November 1985 to May 1991, are examined to study the association of the seasonal variations in the optical depths and their association with the prevailing meteorological conditions. A systematic seasonal variation has been observed, with the optical depths maximising in the summer/pre-monsoon season and reaching a minimum in the winter season. Significant association has been observed between the seasonal variations of aerosol spectral optical depths with those of the (on-shore) surface wind speed and the rainfall. The implications of the findings are discussed.

Research on Multiple Spectral Ranges with Deep Learning for SpO2 Measurement

Oxyhemoglobin saturation by pulse oximetry (SpO2) has always played an important role in the diagnosis of symptoms. Considering that the traditional SpO2 measurement has a certain error due to the number of wavelengths and the algorithm and the wider application of machine learning and spectrum combination, we propose to use 12-wavelength spectral absorption measurement to improve the accuracy of SpO2 measurement. To investigate the multiple spectral regions for deep learning for SpO2 measurement, three datasets for training and verification were built, which were constructed over the spectra of first region, second region, and full region and their sub-regions, respectively. For each region under the procedures of optimization of our model, a thorough of investigation of hyperparameters is proceeded. Additionally, data augmentation is preformed to expand dataset with added noise randomly, increasing the diversity of data and improving the generalization of the neural network. After that, the established dataset is input to a one dimensional convolution neural network (1D-CNN) to obtain a measurement model of SpO2. In order to enhance the model accuracy, GridSearchCV and Bayesian optimization are applied to optimize the hyperparameters. The optimal accuracies of proposed model optimized by GridSearchCV and Bayesian Optimization is 89.3% and 99.4%, respectively, and trained with the dataset at the spectral region of six wavelengths including 650 nm, 680 nm, 730 nm, 760 nm, 810 nm, 860 nm. The total relative error of the best model is only 0.46%, optimized by Bayesian optimization. Although the spectral measurement with more features can improve the resolution ability of the neural network, the results reveal that the training with the dataset of the shorter six wavelength is redundant. This analysis shows that it is very important to construct an effective 1D-CNN model area for spectral measurement using the appropriate spectral ranges and number of wavelengths. It shows that our proposed 1D-CNN model gives a new and feasible approach to measure SpO2 based on multi-wavelength.

Classification of Astrophysics Journal Articles with Machine Learning to Identify Data for NED

The NASA/IPAC Extragalactic Database (NED) is a comprehensive online service that combines fundamental multi-wavelength information for known objects beyond the Milky Way and provides value-added, derived quantities and tools to search and access the data. The contents and relationships between measurements in the database are continuously augmented and revised to stay current with astrophysics literature and new sky surveys. The conventional process of distilling and extracting data from the literature involves human experts to review the journal articles and determine if an article is of extragalactic nature, and if so, what types of data it contains. This is both labor intensive and unsustainable, especially given the ever-increasing number of publications each year. We present here a machine learning (ML) approach developed and integrated into the NED production pipeline to help automate the classification of journal article topics and their data content for inclusion into NED. We show that this ML application can successfully reproduce the classifications of a human expert to an accuracy of over 90% in a fraction of the time it takes a human, allowing us to focus human expertise on tasks that are more difficult to automate.