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The rapid processing, analysis, and mining of remote-sensing big data based on intelligent interpretation technology using remote-sensing cloud computing platforms (RS-CCPs) have recently become a new trend. The existing RS-CCPs mainly focus on developing and optimizing high-performance data storage and intelligent computing for common visual representation, which ignores remote sensing data characteristics such as large image size, large-scale change, multiple data channels, and geographic knowledge embedding, thus impairing computational efficiency and accuracy. We construct a LuoJiaAI platform composed of a standard large-scale sample database (LuoJiaSET) and a dedicated deep learning framework (LuoJiaNET) to achieve state-of-the-art performance on five typical remote sensing interpretation tasks, including scene classification, object detection, land-use classification, change detection, and multi-view 3D reconstruction. The details of the LuoJiaAI application experiment can be found at the white paper for LuoJiaAI industrial application. In addition, LuoJiaAI is an open-source RS-CCP that supports the latest Open Geospatial Consortium (OGC) standards for better developing and sharing Earth Artificial Intelligence (AI) algorithms and products on benchmark datasets. LuoJiaAI narrows the gap between the sample database and deep learning frameworks through a user-friendly data-framework collaboration mechanism, showing great potential in high-precision remote sensing mapping applications. [ABSTRACT FROM AUTHOR]

Artificial Intelligence Machine Learning (AI/ML), in particular Deep Learning (DL), is reorienting and transforming Earth Observation (EO). A consistent data model for delivery of training data will support the FAIR data principles (findable, accessible, interoperable, reusable) and enable Web-based use of training data in a spatial data infrastructure (SDI). Existing training datasets, including open source benchmark datasets, are usually packaged into public or personal repositories and lack discoverability and accessibility. Moreover, there is no unified method to describe the training data. Here we propose a training data model for AI in EO to allow documentation, storage, and sharing of geospatial training data in a distributed infrastructure. We present design rationales, information models, and an encoding method. Several scenarios illustrate the intended uses and benefits for EO DL applications in an open Web environment. The relationship with Open Geospatial Consortium (OGC) standards is also discussed, as is the impact on an AI-ready SDI. [ABSTRACT FROM AUTHOR]

In this paper, we address the distributed resilient fusion filtering (DRFF) problem for a class of nonlinear multi-sensor networked systems (MSNSs) with random sensor delay (RSD) under round-robin protocol (RRP). The RSD is depicted by the Bernoulli random variable with known occurrence probability. In order to relieve the network congestion, the RRP that can deal with information overload issue of the transmission process from sensor to the estimator is utilised. The major objective of this paper is that the resilient fusion filter is designed for nonlinear MSNSs with RSD and RRP in the light of the inverse covariance intersection approach, where the local upper bound regarding the filtering error covariance is obtained and then minimised by suitably exploiting the local filter gain. Finally, a simulation example that can show the validity of the provided DRFF algorithm is presented. [ABSTRACT FROM AUTHOR]

Data management and analysis are challenging with big Earth observation (EO) data. Expanding upon the rising promises of data cubes for analysis-ready big EO data, we propose a new geospatial infrastructure layered over a data cube to facilitate big EO data management and analysis. Compared to previous work on data cubes, the proposed infrastructure, GeoCube, extends the capacity of data cubes to multi-source big vector and raster data. GeoCube is developed in terms of three major efforts: formalize cube dimensions for multi-source geospatial data, process geospatial data query along these dimensions, and organize cube data for high-performance geoprocessing. This strategy improves EO data cube management and keeps connections with the business intelligence cube, which provides supplementary information for EO data cube processing. The paper highlights the major efforts and key research contributions to online analytical processing for dimension formalization, distributed cube objects for tiles, and artificial intelligence enabled prediction of computational intensity for data cube processing. Case studies with data from Landsat, Gaofen, and OpenStreetMap demonstrate the capabilities and applicability of the proposed infrastructure. [ABSTRACT FROM AUTHOR]

In this paper, the design problem of recursive state estimation algorithm based on delay-prediction compensation is considered for a class of linear time-varying uncertain dynamical networks with network-induced communication transmission delays and stochastic coupling phenomenon. The random variables obeying Bernoulli distribution under uncertain occurrence probabilities are selected to depict the stochastic coupling phenomenon. Moreover, the modelling uncertainties and communication transmission delays are taken into account. Afterwards, a feasible prediction equation is constructed to compensate the communication transmission delays, and an updated time-varying state estimator is designed. Subsequently, the upper bound expression of the state estimation error covariance matrix is given by using the stochastic analysis technique and matrix theory, and the gain matrix of time-varying state estimator is obtained via minimizing the trace of such an upper bound. Furthermore, we present the mathematical proof which illustrates the monotonic relationship between the occurrence probabilities of stochastic coupling and the upper bound of error covariance. Finally, a simulation example is utilized to demonstrate the effectiveness of the proposed estimation strategy based on the delay prediction idea. [ABSTRACT FROM AUTHOR]

This article presents an indoor infant monitoring system based on Android system which employs a low-cost Bluetooth Low Energy (BLE) pseudolite to accurately locate the infants in the activity indoors and timely remind the parents. It can achieve a positioning accuracy better than 2 m and effectively reduce the risk of injury to infants and young children. The performance of the proposed system, using false negative rate, false alarm rate, and alarm response time as indicators, is compared with that of the system using Quuppa as a positioning source. The experiment results show that both BLE pseudolite and Quuppa as a positioning source can basically meet the positioning accuracy for the infant monitoring system, but the BLE-pseudolite-based system provides a more cost-efficient solution. [ABSTRACT FROM AUTHOR]

Folic acid-modified carboxymethyl chitosan was polymerized withN-3-acrylamidophenylboronic acid monomer to prepare tumor-targeting nanoparticles (NPs). Nanoparticles’ size was characterized by dynamic light scattering, while the micromorphology was observed through transmission electron microscopy (TEM) and scanning electronic microscope (SEM). Doxorubicin was then loaded into prepared nanoparticles. The cellular uptake of these NPs was investigated in monolayer cell model and multicellular spheroids. The results revealed that FA-modified nanoparticles possess excellent ability of accumulation and penetration in 2D and 3D cell models. It was also found that these nanoparticles enhanced drug accumulation in tumor, which finally increased antitumor efficiency in H22 tumor-bearing mice. [ABSTRACT FROM PUBLISHER]