Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanically tunable coupled resonator waveguide bandpass filter

This paper proposes a novel mechanically tunable waveguide filter design, based on a coupled resonator topology. The resonator geometry consists of a rectangular waveguide cavity and a dielectric cylinder. Unlike conventional cylindrical resonators which are made out of a single part, here the cylinders are cut in half. By changing the distance between these two parts, the resonating frequency of the resonator can be changed, which allows for a tunable filter design. A filter using the proposed geometry has a large tuning bandwidth as well as relatively constant passband frequencies. The performance of the proposed filter is demonstrated by evaluating the filter using 3D simulation software CST Studio Suite.
Visual Question Answering From Remote Sensing Images

Remote sensing images carry wide amounts of information beyond land cover or land use. Images contain visual and structural information that can be queried to obtain high level information about specific image content or relational dependencies between the objects sensed. This paper explores the possibility to use questions formulated in natural language as a generic and accessible way to extract this type of information from remote sensing images, i.e. visual question answering. We introduce an automatic way to create a dataset using OpenStreetMap1 data and present some preliminary results. Our proposed approach is based on deep learning, and is trained using our new dataset.
Energy-Sustainable Fog System for Mobile Web Services in Infrastructure-Less Environments

Nowadays, critical information services such as emergency management and in-situation navigation rely heavily on the assumption of a reliable networking infrastructure and stable cloud processing, which is ineffective in infrastructure-less environments where disrupted or even no telecommunication connectivity is the norm. On the other hand, fog computing is an extension of cloud computing that is at the physical proximity of end-users to enable local storage, computing, and various forms of communication between devices and users. To this end, we design a fog system spanning hardware, software and networking, thus meeting the requirements of various stakeholders in difficult surroundings. As a complement to a cloud-centred solution, this system is geo-distributed, self-powered, self-managed, location-aware, highly efficient and able to provide situational information services without Internet connectivity. The proposed system has been implemented in national parklands of Australia to achieve a personalized information service, emergency management and in-park navigation for all types of parkland users.
Performance of 2-D Deformation Measurements by the Multi-Static Harmony (Stereoid) Mission

This paper debates the performance of the HARMONY (STEREOID) ESA EE-10 candidate mission in measuring the two-dimensional (2D) terrain deformation. Thanks to its Stereo configuration, where the two passive spacecrafts span a large along-track baseline centered on the Sentinel-1 satellite, a large observation angle diversity in azimuth can be achieved. This theoretically leads to promising deformation performance in the north-south direction component, which will play an extremely important role for the surface displacement analysis in the future
Artificial Neural Network in Fibres Length Prediction for High Precision Control of Cellulose Refining

Paper, a web of interconnected cellulose fibres, is widely used as a base substrate. It has been applied in several applications since it features interesting properties, such as renewability, biodegradability, recyclability, affordability and mechanical flexibility. Furthermore, it offers a broad possibility to modify its surface properties toward specifics additives. The fillers retention and the fibres bonding ability are heavily affected by the cellulose refining process that influences chemical and morphological features of the fibres. Several refining theories were developed in order to determine the best refining conditions. However, it is not trivial to control the cellulose refining as different phenomena occur simultaneously. Therefore, it is intuitively managed by experienced papermakers to improve paper structures and properties. An approach based on the machine learning aimed at estimating the effects of refining on the fibres morphology is proposed in this study. In particular, an artificial neural network (ANN) was implemented and trained with experimental data to predict the fibres length as a function of refining process variables. The prediction of this parameter is crucial to obtain a high-performance process in terms of effectiveness and the optimisation of the final product performance as a function of the process parameter. To achieve these results, data mining of the experimental patterns collected was exploited. It led to the achievement of excellent performance and high accuracy in fibres length prediction.
Land Cover Mapping without Human Annotation

The advent of satellite constellations has been rapidly increasing the acquisition frequency of Earth observation images, enabling continuous updates of land cover maps on a global scale with supervised learning. Since annotation of land cover semantic classes is expensive with a field survey, the number of labeled samples and the update frequency are limited. To compensate for the lack of ground-truth labels, we propose to substitute automatically labeled ground-shot images for human annotated data. In our methodology, unlabeled georefer-enced ground-shot images are classified with a Convolutional Neural Network (CNN), which is trained by a set of ground-shot images annotated by humans in advance. We propose a new method that automatically grades human annotation to address noisy labels used for training the CNN. The outcomes of ground-shot image classification are used as labeled samples for pixel-wise land cover classification of multispectral satellite imagery. The resulted land cover classification map resembles the results supervised by human-annotated samples. The experimental results show that our methodology has the potential to associate satellite imagery with ground-shot imagery.
Evaluation of Machine Learning-Based Urban Surface Mapping Using a New Moderate-Resolution Satellite Imagery Dataset

The machine learning algorithm support high efficiency in urban surface mapping based on the moderate resolution and multispectral satellite imagery. In this study, we evaluate eight widely used machine learning-based classification methods. For the specific modality of the moderate resolution and multispectral satellite imagery, nevertheless, even though many labeled datasets target for kinds of remote sensing applications are built recently, the labeled data for the moderate-resolution and multispectral image is still lacked currently. In this study, we carried out a moderate-resolution and multispectral images labeling work in two urban regions, and four-class urban surface and six-class urban surface in terms of the bio-physical and land use are labeled, respectively. Based on the labeled dataset, eight widely used machine learning-based supervised classifiers are selected for the methods evaluation. As the results obtained, the SVM, MLC and ANN achieved the highest accuracy of 88.2%, 85.4% and 84.1% in a simple four-class urban surface mapping experiment. With the increasing requirement of the remote sensing application, more advanced machine learning algorithms would be explored, and the evaluation in our study as well as the labeled dataset can provide a baseline for the future research.
GRU with Spatial Prior for Hyperspectral Image Classification

Neural networks have been successfully used to extract deep features for many hyperspectral tasks. In this study, we propose a tiny effective model based on gate recurrent unit (GRU) with spectral-spatial information for hyperspectral image classification. In our method, the core GRU cell can learn interspectral correlations within an entirely continuous spectrum input, and spatial information is the initial state of this GRU cell as a prior. Experimental results demonstrate that our method can fully utilize spectral and spatial information to obtain competitive performance.
A Design and Analysis Perspective on Architecting Memory Using Domain-Wall Memory

With recent trend of Internet of Things (IoT), the gigantic connectivity of billions of devices would generate a tremendous amount of data. Consequently, a massive number of memories are used for retaining data and the processing result. Recently, memory has become a major contributor to the power consumption, which has brought a constraint in the development main memory design for typical applications such as big-data and IoT. A high requirement for high energy efficiency and high capacity should be met in the IoT development. An emerging non-volatile memory (NVM) technology, Domain-wall memory (DWM), have been proposed as a promising candidate in the replacement of conventional memory owing to its non-volatility and ultra-high density. In this paper, we survey the architectural techniques for using DWM in designing of CPU, GPU and application-specific processors. We also briefly summarize the common experimental platforms in architecting of DWM.
Associations of Leisure-Time Physical Activity Trajectories with Fruit and Vegetable Consumption from Childhood to Adulthood: The Cardiovascular Risk in Young Finns Study

A physically active lifestyle and a diet rich in vegetables and fruits have a central role in promoting health. This study examined the associations between leisure-time physical activity (LTPA) trajectories and fruit and vegetable consumption (FVC) from childhood to middle age. The data were drawn from the Cardiovascular Risk in Young Finns Study with six age cohorts. Participants were 9 to 18 years (n = 3536; 51% females) at baseline in 1980 and 33 to 48 years at the last follow-up in 2011. LTPA and FVC were self-reported. LTPA trajectories were identified using latent profile analyses, after which the mean differences in FVC across the trajectories were studied. Active, low-active, decreasingly and increasingly active trajectories were identified for both genders. An additional trajectory describing inactivity was identified for females. Those who were persistently active or increased their LTPA had higher FVC at many ages when compared to their inactive or low-active counterparts (p < 0.05). In females prior to age 42 and in males prior to age 24, FVC was higher at many ages in those with decreasing activity than in their inactive or low-active counterparts (p < 0.05). The development of LTPA and FVC from childhood to middle age seem to occur in tandem.
Active/Passive Geophysical Model Functions For Ocean Vector Wind Retrievals From TRMM

In this paper, we describe the development of Geophysical Model Functions (GMF) for remote sensing of ocean vector winds using the Precipitation Radar (PR) and TRMM Microwave Imager (TMI) that operated for > 17 years on the Tropical Rainfall Measurement Mission (TRMM) observatory. Wind speed and anisotropic wind direction characteristics of the ocean backscatter and brightness temperature were empirically characterized for 49 PR beam positions and three TMI frequency channels. Temporal stability and sea surface temperature (SST) dependency of the GMFs are also evaluated.
ACIX – Atmospheric Atmospheric Correction Inter-comparison eXercise

ACIX is international collaborative initiatives to inter-compare a set of atmospheric correction (AC) algorithms for high-spatial resolution optical sensors. The exercises focus on Sentinel-2 and Landsat-8 data over a set of test areas. The inter-comparison contributes to a better understanding of strengths and weaknesses of different algorithms and ultimately to contribute to a reduction of a major error source for atmospheric correction and surface parameter retrievals.
Environmental Toxicology and Chemistry

Perfluorooctane sulfonate (PFOS) is an increasingly important environmental pollutant that is pervasive in the environment. A number of studies have focused on the toxicological effects of PFOS on model fish species (zebrafish and medaka), but little is known about the impact of PFOS on commercially important marine fish. Thus, the present study examined transcriptome responses to PFOS exposure in the liver of juvenile Cynoglossus semilaevis, an important farmed flatfish in China. Then, in response to PFOS challenges, 1695 and 5244 genes were identified as significantly increased and depressed, respectively. Significant expression changes were observed in immune‐related genes (cytokine–cytokine receptor interaction, T‐helper [Th]17 cell differentiation, and the chemokine nuclear factor‐kappa B and T‐cell receptor signaling pathways), indicating that immunotoxicity is a key aspect of the effects of PFOS on C. semilaevis. Exposure to PFOS also altered the gene expression levels of hormones (inhibin, insulin, somatostatin, and glucagon), which could lead to severe metabolic and endocrine dysfunction. As expected from previous studies, several phase I and phase II detoxification enzymes were significantly up‐regulated, which could facilitate the biotransformation and detoxification of PFOS in C. semilaevis. The present study provides new insights into the molecular toxicology of PFOS in a commercially important fish species. Environ Toxicol Chem 2020;39:556–564. © 2019 SETAC
High-Efficiency Progressive Transmission and Automatic Recognition of Wildlife Monitoring Images With WISNs

Wireless image sensor networks (WISNs) are widely applied in wildlife monitoring, as they present a better performance in remote, real-time monitoring. However, traditional WISNs suffer from the limitations of low processing capability, power consumption restrictions and narrow transmission bandwidth. For the contradiction between the above limitations of WISNs and the wildlife monitoring images with high resolution and complex background, we propose a novel wildlife intelligent monitoring system. On the foundation of saliency object detection, the convolutional encoder-decoder network is utilized to realize the progressive compression transmission and restoration for wildlife monitoring images, which guarantees the transmission efficiency and quality of wildlife part. Moreover, to deal with the problems of high labor intensity, low efficiency and low recognition accuracy in classical manual sorting method, an improved Faster RCNN algorithm is proposed on the automatic recognition of wildlife images. The experimental results on our own wildlife dataset, show that the peak signal to noise ratio (PSNR) and structural similarity index (SSIM) are improved by 7.93%, 18.15% and 7.01%, 12.67% respectively on reconstruction image, when compared with the set partitioned in hierarchical tree (SPIHT) and embedded zerotree (EZW) algorithms. Compared with the traditional Faster RCNN algorithm, the recognition accuracy of six species wildlife is respectively improved by 1%, 18%, 5%, 17%, 2% and 19%, and the final mAP value reaches to 92.2% in test set increased by 10.9%, which demonstrates the proposed algorithm can ideally achieve the wildlife intelligent monitoring with WISNs.
A Measurement-Division Model for TDOA-Based Source Bearing Estimation With Unknown Propagation Speed

This paper focuses on the bearing estimation problem of far-field signal source via time-difference-of-arrival (TDOA) with a synchronized array in 3-D space. It is usually assumed that the propagation speed (PS) is perfectly known in localization. In reality, only an imperfect knowledge of PS could be obtained. The traditional closed-form solutions without involving PS have the advantage of low complexity, but suffer from low estimation accuracy. A measurement-division model is proposed to offer an alternative solution without the need of the propagation speed. This speed-free model combines two original TDOA measurement equations into a division formula, whose Cramer-Rao lower bound (CRLB) is derived for the observed data. A typical optimization method, i.e. the Levenberg-Marquardt (LM) algorithm is adopted to resolve the nonlinear measurement-division model, resulting in an estimation accuracy improvement because of its iterative search behavior. The theoretical performance of this solution is evaluated in terms of bias and covariance. Simulations are conducted to demonstrate an accuracy advantage of the solution over the related methods.
Lidar-camera Based 3D Obstacle Detection for UGVs

A 3D obstacle detection method based on lidar and camera fusion was studied for the safety and autonomy of unmanned ground vehicles. Traditional 3D object detection methods usually rely on a single sensor, such as directly segmenting an object on point cloud or image. We present a method combining lidar and camera by projecting the lidar points onto the camera and fusing the image information of the neighborhood, then a high-resolution depth map is formed based on the accelerated high-dimensional filtering method. By detecting obstacles on the depth map instead of directly acting on the point cloud, the speed of obstacle clustering is increased. And the detection accuracy is improved due to the improvement of the resolution. We validate our results on the kitti dataset, showing the adaptability of our approach.
Decentralized Gathering of Stochastic, Oblivious Agents on a Grid: A Case Study with 3D M-Blocks

We propose stochastic control policies for gathering a group of embodied agents in a two-dimensional square tile environment. The policies are fully decentralized and can be executed on anonymous, oblivious agents with chirality, but no sense of orientation. The agents require only 4 ternary digits of information. We prove that a group of agents, irrespective of initial positions, will almost surely reach a Pareto optimal configuration in finite time. For one of the control policies, computer simulations show that groups of up to 20 agents consistently reach Pareto optimal configurations, whereas groups of 1000 agents, given the same amount of time, improve the compactness of their configurations on average by 89.20%. The policy also copes well with sensory noise up to a level of 50%. We also present an experimental validation using 6 physical 3D M-Block modules, demonstrating the feasibility of the stochastic control approach in practice.
Generating 3d Point Clouds from a Single SAR Image Using 3D Reconstruction Network

Obtaining the three-dimensional data of the target is very useful for the interpretation and application of the SAR target. This paper proposes a deep learning framework to recover the three-dimensional structure of the target from a single SAR image, which is expressed in the form of 3D point cloud. Due to the small data set of SAR images, the network is combined by two parts. First, the twodimensional image in the optical perspective is predicted from the SAR target image, and then the 3D points of the target is reconstructed based on the pre-trained 3D reconstruction network model from the optical images. The experiment is based on the MSTAR datasets. The results confirm the effectiveness of the three-dimensional reconstruction method.
Mechanical Ventilation for ARDS During Extracorporeal Life Support: Research and Practice.

Ventilator-induced lung injury remains a key contributor to the morbidity and mortality of the acute respiratory distress syndrome. Efforts to minimize this injury are typically limited by the need to preserve adequate gas exchange. In the most severe forms of the syndrome, extracorporeal life support is increasingly being deployed for severe hypoxemia or hypercapnic acidosis refractory to conventional ventilator management strategies. Data from a recent randomized controlled trial, a post-hoc analysis of that trial, a meta-analysis, and a large, international, multicenter observational study, all suggest that extracorporeal life support, when combined with lower tidal volumes and airway pressures than the current standard of care, may improve outcomes compared with conventional management in patients with the most severe forms of the acute respiratory distress syndrome. These findings raise important questions not only about the optimal ventilator strategies for patients receiving extracorporeal support, but how various mechanisms of lung injury in the acute respiratory distress syndrome may potentially be mitigated by ultra-lung-protective ventilation strategies when gas exchange is sufficiently managed with the extracorporeal circuit. Additional studies are needed to more precisely delineate the best strategies for optimizing invasive mechanical ventilation in this patient population.
Assessment of Model-Based Polsar Decompositions

Model-based polarimetric decompositions are often used to generate scene classifications from polarimetric SAR imagery. The original Freeman-Durden model has been modified and improved upon multiple times over the past 2 decades. However, quantitative, in-depth analyses of these incoherent model-based decompositions have lagged in comparison. Here we assess model-based polarimetric decomposition techniques for robustness to variations of the in-scene scattering mechanisms using simulated data sets. The simulated scattering mechanisms, e.g. volume scattering model, polarimetric signal-to-noise, simple multi-bounce models, etc., are known and thus provide "ground truth. " We will illustrate our results by applying select model-based decompositions to both simulated and actual polarimetric SAR imagery.
Design of a Programmable Biomedical Front-End Amplifier with Pseudo Resistors

In this study, we are aiming to achieve using an external signal to create a programmable biomedical front-end system (FEA), Fig. 2. Designing modern active filters and amplifiers, mega to tera ohms resistors and minimized capacitors are required. Therefore, the use of CMOS pseudo resistor will have an advantage of minimizing the layout area used compare to the passive resistor. UMC_0. 25 and TSMC_0. 18 are used in our FEA design and find the best fit between the technology and the pseudo resistor structure. This paper presents a voltage control programmable pseudo resistor to directly control the whole FEA system.
Towards Utilizing Deep Uncertainty In Traditional SLAM

Recent advances in Simultaneous Localization and Mapping (SLAM) systems make robots behave intelligently in different situations. However, performing well in dynamic environments requires extraction of robust features from the static context of consecutive frames. Deep Learning, nowadays, helps to extract meaningful and robust feature representations. Using various Deep Learning architectures and retrieving the uncertainty information concerning dynamic objects of the scene, and the motion flow of the environment helps traditional SLAM extract robust features in dynamic environments. To our knowledge, this paper is the first paper to fuse various deep uncertainties in traditional SLAM to obtain robust features in dynamic environments and improve the accuracy of motion estimation for autonomous vehicles.