Research on Real-time Sensing Technology of Aerospace Interactive Behavior Based on Infrared Array Force Tactile

Aerospace robots can replace astronauts to complete dangerous work in the expanded space. How to efficiently and reliably interact with robots in complex and harsh space environment has become an urgent problem and technical challenge. Based on the infrared array force tactile technology, an infrared point force touch detection system is developed to realize the intention sensing of aerospace human-robot interaction. Based on multipoint positioning technology combined with image method and global combined optimal tracking method, the control software is designed, and the effective human-robot interaction in the process of cooperation is realized. Finally, the touch panel test effect of the system is given. This research can provide a theoretical basis for the practical application of aerospace human-robot interaction.

Galaxy Stellar Mass Functions from z ∼ 10 to z ∼ 6 using the Deepest Spitzer/Infrared Array Camera Data: No Significant Evolution in the Stellar-to-halo Mass Ratio of Galaxies in the First Gigayear of Cosmic Time

We present new stellar mass functions at z ∼ 6, z ∼ 7, z ∼ 8, z ∼ 9 and, for the first time, z ∼ 10, constructed from ∼800 Lyman-break galaxies previously identified over the eXtreme Deep Field and Hubble Ultra-Deep Field parallel fields and the five Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey fields. Our study is distinctive due to (1) the much deeper (∼200 hr) wide-area Spitzer/Infrared Array Camera (IRAC) imaging at 3.6 μm and 4.5 μm from the Great Observatories Origins Deep Survey Re-ionization Era Wide-area Treasury from Spitzer program (GREATS) and (2) consideration of z ∼ 6–10 sources over a 3× larger area than those of previous Hubble Space Telescope+Spitzer studies. The Spitzer/IRAC data enable ≥2σ rest-frame optical detections for an unprecedented 50% of galaxies down to a stellar mass limit of ∼ 10 8  ⊙ across all redshifts. Schechter fits to our volume densities suggest a combined evolution in the characteristic mass  * and normalization factor ϕ * between z ∼ 6 and z ∼ 8. The stellar mass density (SMD) increases by ∼1000× in the ∼500 Myr between z ∼ 10 and z ∼ 6, with indications of a steeper evolution between z ∼ 10 and z ∼ 8, similar to the previously reported trend of the star formation rate density. Strikingly, abundance matching to the Bolshoi–Planck simulation indicates halo mass densities evolving at approximately the same rate as the SMD between z ∼ 10 and z ∼ 4. Our results show that the stellar-to-halo mass ratios, a proxy for the star formation efficiency, do not change significantly over the huge stellar mass buildup occurred from z ∼ 10 to z ∼ 6, indicating that the assembly of stellar mass closely mirrors the buildup in halo mass in the first ∼1 Gyr of cosmic history. The James Webb Space Telescope is poised to extend these results into the “first galaxy” epoch at z ≳ 10.

Device-Free Human Activity Recognition with Low-Resolution Infrared Array Sensor Using Long Short-Term Memory Neural Network

Sensor-based human activity recognition (HAR) has attracted enormous interests due to its wide applications in the Internet of Things (IoT), smart homes and healthcare. In this paper, a low-resolution infrared array sensor-based HAR approach is proposed using the deep learning framework. The device-free sensing system leverages the infrared array sensor of 8×8 pixels to collect the infrared signals, which can ensure users’ privacy and effectively reduce the deployment cost of the network. To reduce the influence of temperature variations, a combination of the J-filter noise reduction method and the Butterworth filter is performed to preprocess the infrared signals. Long short-term memory (LSTM), a representative recurrent neural network, is utilized to automatically extract characteristics from the infrared signal and build the recognition model. In addition, the real-time HAR interface is designed by embedding the LSTM model. Experimental results show that the typical daily activities can be classified with the recognition accuracy of 98.287%. The proposed approach yields a better result compared to the existing machine learning methods, and it provides a low-cost yet promising solution for privacy-preserving scenarios.

A Spitzer survey of Deep Drilling Fields to be targeted by the Vera C. Rubin Observatory Legacy Survey of Space and Time

ABSTRACT The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will observe several Deep Drilling Fields (DDFs) to a greater depth and with a more rapid cadence than the main survey. In this paper, we describe the ‘DeepDrill’ survey, which used the Spitzer Space Telescope Infrared Array Camera (IRAC) to observe three of the four currently defined DDFs in two bands, centred on 3.6 and 4.5 μm. These observations expand the area that was covered by an earlier set of observations in these three fields by the Spitzer Extragalactic Representative Volume Survey (SERVS). The combined DeepDrill and SERVS data cover the footprints of the LSST DDFs in the Extended Chandra Deep Field–South (ECDFS) field, the ELAIS-S1 field (ES1), and the XMM-Large-Scale Structure Survey field (XMM-LSS). The observations reach an approximate 5σ point-source depth of 2 μJy (corresponding to an AB magnitude of 23.1; sufficient to detect a 10$^{11} \, \mathrm{M}_{\odot}$ galaxy out to z ≈ 5) in each of the two bands over a total area of $\approx 29\,$ deg2. The dual-band catalogues contain a total of 2.35 million sources. In this paper, we describe the observations and data products from the survey, and an overview of the properties of galaxies in the survey. We compare the source counts to predictions from the Shark semi-analytic model of galaxy formation. We also identify a population of sources with extremely red ([3.6]−[4.5] >1.2) colours which we show mostly consists of highly obscured active galactic nuclei.

Privacy-Preserved Fall Detection Method with Three-Dimensional Convolutional Neural Network Using Low-Resolution Infrared Array Sensor

Due to the rapid aging of the population in recent years, the number of elderly people in hospitals and nursing homes is increasing, which results in a shortage of staff. Therefore, the situation of elderly citizens requires real-time attention, especially when dangerous situations such as falls occur. If staff cannot find and deal with them promptly, it might become a serious problem. For such a situation, many kinds of human motion detection systems have been in development, many of which are based on portable devices attached to a user’s body or external sensing devices such as cameras. However, portable devices can be inconvenient for users, while optical cameras are affected by lighting conditions and face privacy issues. In this study, a human motion detection system using a low-resolution infrared array sensor was developed to protect the safety and privacy of people who need to be cared for in hospitals and nursing homes. The proposed system can overcome the above limitations and have a wide range of application. The system can detect eight kinds of motions, of which falling is the most dangerous, by using a three-dimensional convolutional neural network. As a result of experiments of 16 participants and cross-validations of fall detection, the proposed method could achieve 98.8% and 94.9% of accuracy and F1-measure, respectively. They were 1% and 3.6% higher than those of a long short-term memory network, and show feasibility of real-time practical application.