GNSS Application in Retrieving Sea Wind Speed

In traditional Global Navigation Satellite System (GNSS) application, the reflected GNSS signals from Earth’s surface generally are considered as an interference source to be suppressed or removed. Recently, a new idea which treats the reflected GNSS signal as opportunity source of remote sensing has been proposed to monitor Earth’s physical parameters. This technique is called as GNSS-Reflectometry (GNSS-R) which has the advantages of low-power, -mass and -cost. With the development and modernization of GPS, Galileo, GLONASS, and BeiDou system, spaceborne GNSS could significantly improve the temporal-spatial resolution by receiving and processing the reflected signal from multiple satellites. This chapter mainly describes this new bi-static remote sensing technique. First, basic theories of GNSS-R including spatial geometry, polarization, and scattering model of reflected signal are discussed; second, spaceborne receivers and fast-response processing methods are reviewed and analyzed; finally, the empirical models retrieving wind speed are proposed and demonstrated using the DDM data from the UK-TechDomeSat-1 satellite. Based on the discussion of this chapter, it could be concluded that although GNSS-R still has some key challenges which have to be addressed, it could be an optimal choice of remote sensing in some special conditions, such as the tropical cyclone

An Entire Renal Anatomy Extraction Network for Advanced CAD During Partial Nephrectomy

Partial nephrectomy (PN) is common surgery in urology. Digitization of renal anatomies brings much help to many computer-aided diagnosis (CAD) techniques during PN. However, the manual delineation of kidney vascular system and tumor on each slice is time consuming, error-prone, and inconsistent. Therefore, we proposed an entire renal anatomies extraction method from Computed Tomographic Angiographic (CTA) images fully based on deep learning. We adopted a coarse-to-fine workflow to extract target tissues: first, we roughly located the kidney region, and then cropped the kidney region for more detail extraction. The network we used in our workflow is based on 3D U-Net. To dealing with the imbalance of class contributions to loss, we combined the dice loss with focal loss, and added an extra weight to prevent excessive attention. We also improved the manual annotations of vessels by merging semi-trained model's prediction and original annotations under supervision. We performed several experiments to find the best-fitting combination of variables for training. We trained and evaluated the models on our 60 cases dataset with 3 different sources. The average dice score coefficient (DSC) of kidney, tumor, cyst, artery, and vein, were 90.9%, 90.0%, 89.2%, 80.1% and 82.2% respectively. Our modulate weight and hybrid strategy of loss function increased the average DSC of all tissues about 8-20%. Our optimization of vessel annotation improved the average DSC about 1-5%. We proved the efficiency of our network on renal anatomies segmentation. The high accuracy and fully automation make it possible to quickly digitize the personal renal anatomies, which greatly increases the feasibility and practicability of CAD application on urology surgery

Manipulating Thermal Conductivity via Targeted Phonon Excitation

Thermal conductivity is a critical property for materials in many practical applications, such as thermoelectric devices and heat dissipation. It has become an issue of great concern how thermal conductivity can be effectively manipulated. In this work, we manipulate the thermal conductivities of graphene and graphene nanoribbon via targeted phonon quantal excitation. Ab initio calculations show that the thermal conductivity of graphene can be tailored in the range of 45% to 155%, compared with the intrinsic value. Molecular dynamics simulations also exhibit a similar trend (82%-124%) for graphene nanoribbon. This strategy provides a new way for manipulating thermal conductivity in-situ without changing the composition of materials

The Impact of Inter-Modulation Components on Interferometric GNSS-Reflectometry

The interferometric Global Navigation Satellite System Reflectometry (iGNSS-R) exploits the full spectrum of the transmitted GNSS signal to improve the ranging performance for sea surface height applications. The Inter-Modulation (IM) component of the GNSS signals is an additional component that keeps the power envelope of the composite signals constant. This extra component has been neglected in previous studies on iGNSS-R, in both modelling and instrumentation. This letter takes the GPS L1 signal as an example to analyse the impact of the IM component on iGNSS-R ocean altimetry, including signal-to-noise ratio, the altimetric sensitivity and the final altimetric precision. Analytical results show that previous estimates of the final altimetric precision were underestimated by a factor of 1 . 5 ∼ 1 . 7 due to the negligence of the IM component, which should be taken into account in proper design of the future spaceborne iGNSS-R altimetry missions.This work was supported in part by the European Space Agency (ESTEC RFP/IPL- PTE/FE/yc/1157/2015) and in part by the Spanish Ministry of Economy and Competitiveness (ESP2015-70014-C2-2-R). We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI)

Evaluation of Chinese Quad-polarization Gaofen-3 SAR Wave Mode Data for Significant Wave Height Retrieval

Our work describes the accuracy of Chinese quad-polarization Gaofen-3 (GF-3) synthetic aperture radar (SAR) wave mode data for wave retrieval and provides guidance for the operational applications of GF-3 SAR. In this study, we evaluated the accuracy of the SAR-derived significant wave height (SWH) from 10,514 GF-3 SAR images with visible wave streaks acquired in wave mode by using the existing wave retrieval algorithms, e.g., the theoretical-based algorithm parameterized first-guess spectrum method (PFSM), the empirical algorithm CSAR_WAVE2 for VV-polarization, and the algorithm for quad-polarization (Q-P). The retrieved SWHs were compared with the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis field with 0.125° grids. The root mean square error (RMSE) of the SWH is 0.57 m, found using CSAR_WAVE2, and this RMSE value was less than the RMSE values for the analysis results achieved with the PFSM and Q-P algorithms. The statistical analysis also indicated that wind speed had little impact on the bias with increasing wind speed. However, the retrieval tended to overestimate when the SWH was smaller than 2.5 m and underestimate with an increasing SWH. This behavior provides a perspective of the improvement needed for the SWH retrieval algorithm using the GF-3 SAR acquired in wave mode

The Application of Interdisciplinary In Airborne Electromechanical System and Its Enlightenment to the Cultivation of Graduate Students’ Innovative Ability

With the progress of science and technology, intelligent monitoring and diagnosis system has developed rapidly. Intelligent diagnosis technology, as an engineering application of artificial intelligence, has developed rapidly both at home and abroad in recent years. Research shows that intelligent diagnosis technology is a comprehensive industry integrating multiple technologies and interdisciplinary disciplines, and it is also a partial epitome of contemporary scientific and technological progress. Combined with the development of intelligent diagnosis technology of airborne electromechanical system and the important task of colleges and universities as the undertaker of high-end talent training, this paper puts forward that the current talent training mode needs to be adjusted according to the needs of science and technology, and teaching practice reform should be carried out from professional fields, discipline categories, practical training platforms and other aspects, so as to provide reserve talents for China's scientific and technological progress

OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System

Automated machine learning (AutoML) seeks to build ML models with minimal human effort. While considerable research has been conducted in the area of AutoML in general, aiming to take humans out of the loop when building artificial intelligence (AI) applications, scant literature has focused on how AutoML works well in open-environment scenarios such as the process of training and updating large models, industrial supply chains or the industrial metaverse, where people often face open-loop problems during the search process: they must continuously collect data, update data and models, satisfy the requirements of the development and deployment environment, support massive devices, modify evaluation metrics, etc. Addressing the open-environment issue with pure data-driven approaches requires considerable data, computing resources, and effort from dedicated data engineers, making current AutoML systems and platforms inefficient and computationally intractable. Human-computer interaction is a practical and feasible way to tackle the problem of open-environment AI. In this paper, we introduce OmniForce, a human-centered AutoML (HAML) system that yields both human-assisted ML and ML-assisted human techniques, to put an AutoML system into practice and build adaptive AI in open-environment scenarios. Specifically, we present OmniForce in terms of ML version management; pipeline-driven development and deployment collaborations; a flexible search strategy framework; and widely provisioned and crowdsourced application algorithms, including large models. Furthermore, the (large) models constructed by OmniForce can be automatically turned into remote services in a few minutes; this process is dubbed model as a service (MaaS). Experimental results obtained in multiple search spaces and real-world use cases demonstrate the efficacy and efficiency of OmniForce

Retrieval and Assessment of Significant Wave Height from CYGNSS Mission Using Neural Network

In this study, we investigate sea state estimation from spaceborne GNSS-R. Due to the complex scattering of electromagnetic waves on the rough sea surface, the neural network approach is adopted to develop an algorithm to derive significant wave height (SWH) from CYGNSS data. Eighty-nine million pieces of CYGNSS data from September to November 2020 and the co-located ECMWF data are employed to train a three-hidden-layer neural network. Ten variables are considered as the input parameters of the neural network. Without the auxiliary of the wind speed, the SWH retrieved using the trained neural network exhibits a bias and an RMSE of −0.13 and 0.59 m with respect to ECMWF data. When considering wind speed as the input, the bias and RMSE were reduced to −0.09 and 0.49 m, respectively. When the incidence angle ranges from 35° to 65° and the SNR is above 7 dB, the retrieval performance is better than that obtained using other values. The measurements derived from the “Block III” satellite offer worse results than those derived from other satellites. When the distance is considered as an input parameter, the retrieval performances for the areas near the coast are significantly improved. A soft data filter is used to synchronously improve the precision and ensure the desired sample number. The RMSEs of the retrieved SWH are reduced to 0.45 m and 0.41 m from 0.59 m and 0.49 m, and only 16.0% and 14.9% of the samples are removed. The retrieved SWH also shows a clear agreement with the co-located buoy and Jason-3 altimeter data

Retrieval and Assessment of Significant Wave Height from CYGNSS Mission Using Neural Network

In this study, we investigate sea state estimation from spaceborne GNSS-R. Due to the complex scattering of electromagnetic waves on the rough sea surface, the neural network approach is adopted to develop an algorithm to derive significant wave height (SWH) from CYGNSS data. Eighty-nine million pieces of CYGNSS data from September to November 2020 and the co-located ECMWF data are employed to train a three-hidden-layer neural network. Ten variables are considered as the input parameters of the neural network. Without the auxiliary of the wind speed, the SWH retrieved using the trained neural network exhibits a bias and an RMSE of −0.13 and 0.59 m with respect to ECMWF data. When considering wind speed as the input, the bias and RMSE were reduced to −0.09 and 0.49 m, respectively. When the incidence angle ranges from 35° to 65° and the SNR is above 7 dB, the retrieval performance is better than that obtained using other values. The measurements derived from the “Block III” satellite offer worse results than those derived from other satellites. When the distance is considered as an input parameter, the retrieval performances for the areas near the coast are significantly improved. A soft data filter is used to synchronously improve the precision and ensure the desired sample number. The RMSEs of the retrieved SWH are reduced to 0.45 m and 0.41 m from 0.59 m and 0.49 m, and only 16.0% and 14.9% of the samples are removed. The retrieved SWH also shows a clear agreement with the co-located buoy and Jason-3 altimeter data