Miracle-dz/UniFAR-FTUs huggingface dataset:
Photogram Classification-Based Emotion Recognition

This paper presents a method for facial emotion recognition based on parameterized photograms and machine learning techniques. Videos of people displaying emotions are parameterized by a facial feature-based emotional category association process to determine whether a given photogram expresses emotions by comparing the facial action units displayed with findings in the literature about facial emotion. To test the proposed approach, two strategies are adopted. First, photograms displaying emotions are gathered, and then different machine learning classifiers are applied to check the goodness of the obtained set of categorized emotional photograms. Second, classifiers trained on the sets of emotional photograms were then used to emotionally classify all the videos in each database, using all the photograms with no preprocessing or photogram selection. The presented method was tested using the OpenFace parameterizer with emotional videos gathered from Multimedia Understanding Facial Expression (MUG) and Cohn-Kanade (CK+) databases. The outcomes achieved for emotional photogram classification on the sets of emotional photograms reached maximums of 99.80% and 99.63% in the MUG and CK+ databases, respectively. The videos were classified using different voting strategies regarding the outcome of each photogram in the video with all the photogram emotion recognition classifiers obtained results reflecting recognition rates of 70.71% and 66.36% for the videos in MUG and CK+ databases, and reached up to 72.55% and 88.37% when classifier combination strategies were used. The work carried out opens the door to follow-up work concerning data preprocessing and the use of different classifier combination methods in facial emotion recognition.
Random Forest Outperformed Convolutional Neural Networks for Shrub Willow Above Ground Biomass Estimation Using Multi-Spectral UAS Imagery

Shrub willow is a valuable source of hardwood biomass feedstock which is used for the production of bioenergy, biofuels, and renewable bio-based products. The biomass produced from this short-rotation woody plant can be used for heat and electricity generation. Thus, an accurate estimation of shrub willow above-ground biomass (AGB) is of paramount importance. This paper aimed to estimate shrub willow AGB using multi-spectral unmanned aerial system (UAS) imagery and machine learning techniques. To accomplish this goal, a machine learning model (i.e., random forest (RF)) and a deep learning method (i.e., convolutional neural network (CNN)) were applied to the spectral bands and some vegetation indices over a site in Camillus, NY, US in July 2019. The results demonstrated the superiority of the RF model (RMSE of 1.73 Mg/ha and R2 of 0.95) compared to the CNN (RMSE of 2.69 Mg/ha and R2 of 0.89) technique. Adding vegetation indices to spectral bands and using a convolutional approach for training purposes could significantly improve the modeling efficiency.
Himawari Thermal Anomaly Scrutiny with Deep Learning

In the presented article, machine learning (ML) is employed on advanced Himawari imager (AHI) to examine real-time fire and map damaged zone over Yunnan, China. The main emphasis lies in employing machine learning as an alternative to primeval thresholding, extricating, and scrutinizing thermal anomaly using infrared (IR). Firstly, Himawari brightness temperature (BT), band ratio, albedo, and BT differences are utilized to scrutinize fire break out. Then, to ensure pixels are clear and free from clouds, the Himawari cloud product is implemented. Finally, machine learning models such as random forest (RF), artificial neural network (ANN), and time-series long short-term memory (LSTM), a deep learning model, are used to precisely classify the active fire pixels and achieved accuracies of 0.96%, 0.95%, 0.92% respectively. The results evaluated using another AHI wildfire product and inter-compared with multi-source fire products.
CERC-HAND-D: A Tool for Supporting On-the-Fly Flood Mapping in Canada

A custom ArcPro tool, the CERC-HAND-D tool, was developed to create synthetic rating curves (SRCs) using accessible data in Canada. SRCs could support the integration of the Height Above Nearest Drainage (HAND) model into an On-The-Fly flood mapping application for a Canada-wide service. A challenge with creating SRCs is finding an accurate method of representing multiple surface roughness coefficients ($n$) present in a study area. Two distributed $n$ methods were experimented with (a weighted method and a minimum-median method) and were compared to a fixed single $n$ method using CERC-HAND-D. Control data was sourced from gauge stations across Eastern and Central Canada. Results indicate that in areas with medium to medium-high river gradients $(\mathrm{S} > 0.002\ \ \mathrm{m}/\mathrm{m})$ and river lengths under 5 km, the CERC-HAND-D tool creates more accurate SRCs $(\text{NRMSE}=3.7\%-7.0\%, \text{Percent Bias} =-3.7\% -11.5\%)$, with no $n$ method outperforming the other two.
Anti-Jamming Game to Combat Intelligent Jamming for Cognitive Radio Networks

Cognitive Radio (CR) provides a promising solution to the spectrum scarcity problem in dense wireless networks, where the sensing ability of cognitive users helps acquire knowledge of the environment. However, cognitive users are vulnerable to different types of attacks, due to its shared medium. In particular, jamming is considered as one of the most challenging security threats in CR networks. In jamming, an attacker jams the communication by transmitting a high power noise signal in the vicinity of the targeted node. The jammer could be an intelligent entity that is capable of exploiting the dynamics of the environment. In this work, we provide a machine-learning-based anti-jamming technique for CR networks to avoid a hostile jammer, where both the jamming and anti-jamming processes are formulated based on the Markov game framework. In our framework, secondary users avoid the jammer by maximizing its payoff function using an online, model-free reinforcement learning technique called Q-learning. We consider a realistic mathematical model, where the channel conditions are time-varying and differ from one sub-channel to another, as in practical scenarios. Simulation results show that our proposed approach outperforms existing approaches to combat jamming over a wide range of scenarios.
Fully Unsupervised Bi-Temporal Change Detection Framework for VHR SAR

Owing to the unique all-weather, day-night imaging capabilities of Synthetic Aperture Radar (SAR) imaging, the modality is advantageous in the detection of anthropogenic activity. In this paper we present a fully unsupervised change detection framework that operates on Very High Resolution (VHR) SAR image pairs to produce a binary change map, without a need for per-image parameter setting. The framework is demonstrated on a pair of Capella-2 VHR X-band imagery acquired over San Diego, USA.
Model-based System Architecture for Event-triggered Wireless Control of Bio-analytical Devices

Bio-analytical devices have gained importance over the past few years because of their application in rapid diagnostics and biochemical analysis. The integration of the Cyber-Physical System (CPS) concept with bio-analytical devices is essential to enable automation of the device. Modeling of CPS-based bio-analytical devices can provide a deeper understanding of system behavior at early design stages and avoid costly iterations. In this paper, a model-based system architecture enabling wireless control of bio-analytical devices is proposed using the extended timed automata-based formal technique. Using this formal technique, a study case "A droplet flow cytometer for antibiotic susceptibility testing of bacteria" is modeled and verified using the UPPAAL tool. The synchronized operation of the modeled system under defined constraints was confirmed. In addition, the case study shows the implications of formal techniques for the design and verification of wireless automation of high-throughput laboratory setups in Model-Based System Engineering (MBSE).
Multimodal Data Fusion of Social Media and Satellite Images for Emergency Response and Decision-Making

Artificial Intelligence (AI) is already part of our lives and is extensively entering the space sector to offer value-added Earth Observation (EO) products and services. The Copernicus programme provides data on a free, full and open basis, while the recently launched Data and Information Access Service (DIAS) providers index, store and exchange tremendous amounts of data and cloud infrastructure computational resources. Copernicus data and other georeferenced data sources are often highly heterogeneous, distributed and semantically fragmented. One example is the massively generated social media data from citizen observations, including visual, textual and spatiotemporal information. Social media information offers reliable, timely and very prescriptive information about a crisis event. In this work we present the multimodal fusion aspects for combining satellite images and social media for emergency response, such as flood monitoring and extreme weather conditions in polar regions.
Exploring the Potential of Sentinel-2 Data for Tree Crown Mapping in Oak Agro-Forestry Systems

Southern Portugal is characterized by disperse tree cover of Cork and Holm oaks in an agro-forestry system known as montado. Mapping these trees has been historically very difficult as they occur in isolation or in groups with different understory vegetation, including grass and shrubland. Automatic classification for binary tree/non-tree map production has been used elsewhere, but with limited success in the context of montado. Here, the potential of Sentinel-2 data was explored to map oaks using pure and mixed pixels to train a random forest. The output depicts a gradient of tree cover that can be transformed into a crisp map. The accuracy assessment of the latter shows commission and omission errors of 17% and 18%.
READYS: A Reinforcement Learning Based Strategy for Heterogeneous Dynamic Scheduling

In this paper, we propose READYS, a reinforcement learning algorithm for the dynamic scheduling of computations modeled as a Directed Acyclic Graph (DAGs). Our goal is to develop a scheduling algorithm in which allocation and scheduling decisions are made at runtime, based on the state of the system, as performed in runtime systems such as StarPU or ParSEC. Reinforcement Learning is a natural candidate to achieve this task, since its general principle is to build step by step a strategy that, given the state of the system (the state of the resources and a view of the ready tasks and their successors in our case), makes a decision to optimize a global criterion. Moreover, the use of Reinforcement Learning is natural in a context where the duration of tasks (and communications) is stochastic. We propose READYS that combines Graph Convolutional Networks (GCN) with an Actor-Critic Algorithm (A2C): it builds an adaptive representation of the scheduling problem on the fly and learns a scheduling strategy, aiming at minimizing the makespan. A crucial point is that READYS builds a general scheduling strategy which is neither limited to only one specific application or task graph nor one particular problem size, and that can be used to schedule any DAG. We focus on different types of task graphs originating from linear algebra factorization kernels (CHOLESKY, LU, QR) and we consider heterogeneous platforms made of a few CPUs and GPUs. We first propose to analyze the performance of READYS when learning is performed on a given (platform, kernel, problem size) combination. Using simulations, we show that the scheduling agent obtains performances very similar or even superior to algorithms from the literature, and that it is especially powerful when the scheduling environment contains a lot of uncertainty. We additionally demonstrate that our agent exhibits very promising generalization capabilities. To the best of our knowledge, this is the first paper which shows that reinforcement learning can really be used for dynamic DAG scheduling on heterogeneous resources.
Research on Fracture Recognition in Well Logging Images: Adversarial Learning with Attention

Semantic recognition of fractures in well logging images is vital for engineers to implement oil and gas exploration. An essential approach to accomplishing the object is to adopt deep learning based on convolutional neural networks. However, due to the lack of annotated labels in well logging images, it is scarcely available to directly train the semantic segmentation network. In this paper, we explore a domain shift model attempting to achieve domain adaptation from one annotated dataset to our target well logging images. This model's core is to utilize adversarial learning, including generator and discriminator, which can prompt the model to generate fracture segmentation similar to the source domain in the target domain. For enhancing the domain adaptive model further, an attention module is introduced, which can suppress redundant noise in semantic segmentation results. We demonstrate that our proposed model performs well by extensive tests and ablation experiments in the ultrasonic well logging images.
Biomass End-to-End PErformance Simulator: Description of the Ionosphere Module

ESA's BIOMASS Earth Explorer mission aims at providing global above ground forest biomass. Its payload is a UHF synthetic aperture radar. At UHF ionospheric effects, notably the group delay and phase advance, the Faraday rotation, and the intensity and phase scintillations (or rapid fluctuations) are significant and have to be properly modelled to be compensated, to understand the limitations of the proposed technique, and to select the optimum observation conditions. This paper summarizes the structure and main characteristics of the ionospheric module of the BIOMASS end-to-end performance simulator (BEEPS-IOM).
A Cooperative Classification Method for Hyperspectral Images Based on Adaptive Correction

Spatial-spectral classification is an important research direction in hyperspectral image (HSI) processing, but HSI generally cannot provide enough spatial information because of its lower spatial resolution. To improve the classification accuracy on HSI, we propose an adaptive correction strategy on a cooperative classification framework under the assist of multispectral image (MSI), and this strategy adaptively selects the most suitable guided filter to do decision correction for each category. Experimental results demonstrate the effectiveness of the proposed method on cooperative classification.
Remote Sensing Inversion of PM10 Based on Spark Platform

With the continuous growth of remote sensing data and the application of fast and effective atmosphere remote sensing inversion algorithm, this paper proposes a PM10 fast inversion approach based on Spark platform which uses Apache Spark as the analytics engine and integrates with the traditional atmospheric remote sensing inversion algorithm. We first store aerosol data which is MYD04_3K from NASA into HDFS. Then the inversion algorithm is combined with Spark via the function interface to realise rapid atmospheric remote sensing inversion. The experimental results based on Spark platform are compared with those obtained from the traditional physical hardware. The results prove that the proposed atmospheric remote sensing inversion method based on Spark has high efficiency.
Research on Leaf Area Index Extraction Algorithm Based on 3D Reconstruction

The indirect estimation of leaf area index (LAI) is important to agricultural science and ecological science. In this paper, we reconstructed the three-dimensional (3D) structure of the plant based on 3D reconstruct technology, such as the Structure from Motion (SFM) and the multiview stereo (MVS). A LAI measurement method was also proposed based on the ratio of the leaf point cloud area to the ground area. The experiment results showed that the R2 between the true value of the leaf area (LI-3000) and the measured value is 0.9657; compared with the true LAI value calculated based on the leaf area, the relative error of the LAI value extracted from the 3D model is about 44.4%. After removing the stems and smoothing the leaves, the R2 between the true value of the leaf area and the measured value reached 0.9741; the relative error of the measured value of LAI is 9.13%. The experimental results also implied that the deviation of the LAI measurement value mainly comes from the incomplete reconstruction of the covered part of the plant, the influence of the stem, and the smoothness of the leaf surfaces after reconstruction.
Automatic Detection of Inland Water Bodies Along Altimetry Tracks Using Radar Backscattering

Radar altimetry is commonly used to derive water levels over inland water bodies. If lakes and rivers are increasingly covered with radar altimetry virtual stations, wetlands and floodplains are still poorly monitored using this technique. In this study, an unsupervised classification of Ku-band radar altimetry backscattering coefficients from ENVISAT and Jason-2 is performed in the Congo Cuvette Centrale. Comparisons performed against a classification map of the study area shows a good agreement between the water and vegetation classes of the two datasets. Based on these results, radar altimetry-derived water levels are automatically derived over the water classes. Comparisons against radar altimetry-based water stages from Hydroweb database exhibits also a good agreement.
Gradient-Based NMF Methods for Hyperspectral Unmixing Addressing Spectral Variability with a Multiplicative-Tuning Linear Mixing Model

Spectral unmixing methods aim at estimating a collection of pure material spectra and their associated proportions in each pixel of the image. Such methods usually suppose that each pure material is represented by a unique spectrum in all image pixels. However, in many cases this assumption is no more valid for materials that exhibit spectral variability due to varying illumination and atmospheric conditions or material composition. Recently, a novel linear mixing model that handles the spectral variability phenomenon, modeled in a multiplicative form, was proposed with an associated unmixing method. In the present paper, two gradient-based approaches, which use the above mixing model and that are based on pixel-by-pixel nonnegative matrix factorization, are proposed. The first approach uses the projected gradient descent algorithm, whereas the second one employs a Newton update. The proposed algorithms minimize a cost function that takes into account the considered linear mixing model with the spectral variability phenomenon. Experiments, based on realistic synthetic data, are conducted to evaluate the performance of the proposed algorithms. The obtained results are also compared to those of methods from the literature. These test results show that the proposed approaches prove to be very attractive for unmixing hyperspectral remote sensing data with spectral variability.
Generation of Large Scale 3-D City Models Using Insar and Optical Data

Interferometric synthetic aperture radar (InSAR) techniques are powerful tool for reconstructing the 3-D position of scatterers, especially for the urban areas. Since the estimation accuracy depends on the inverse of number of interferograms and signal-to-noise ratio (SNR), it is necessary to use as many as possible interferograms in order to achieve more accurate result. However, the number of interferograms of TanDEM-X data is generally limited for most areas. Therefore, in order to maintain the estimation accuracy, one feasible way is to increase the SNR. In this work, we proposed a novel framework, which integrates the non-local procedure into SAR tomography inversion and combines the robust estimation. A large-scale demonstration has been carried out with five TanDEM-X bistatic data, which covers the entire city of Munich, Germany. Quantitative evaluation of the reconstructed result with the LiDAR reference exhibits the standard deviation of the height difference is within two meters, which implies the proposed framework has great potential for high quality large-scale 3-D urban modeling.
Spatial calibration of whole-body artificial skin on a humanoid robot: comparing self-contact, 3D reconstruction, and CAD-based calibration

Robots were largely missing the sense of touch for decades. As artificial sensitive skins covering large areas of robot bodies are starting to appear, to be useful to the machines, sensor positions on the robot body are needed. In this work, a Nao humanoid robot was retrofitted with pressure-sensitive skin on the head, torso, and arms. We experimentally compare the accuracy and effort associated with the following skin spatial calibration approaches and their combinations: (i) combining CAD models and skin layout in 2D, (ii) 3D reconstruction from images, (iii) using robot kinematics to calibrate skin by self-contact. To acquire 3D positions of taxels on individual skin parts, methods (i) and (ii) were similarly laborious but 3D reconstruction was more accurate. To align these 3D point clouds with the robot kinematics, two variants of self-contact were employed: skin-on-skin and utilization of a custom end effector (finger). In combination with the 3D reconstruction data, mean calibration errors below the radius of individual sensors were achieved (2 mm). Significant perturbation of more than 100 torso taxel positions could be corrected using self-contact calibration, reaching approx. 3 mm mean error. This work is not a proof of concept but deployment of the approaches at scale: the outcome is actual spatial calibration of all 970 taxels on the robot body. As the different calibration approaches are evaluated in isolation as well as in different combinations, this work provides a guideline applicable to spatial calibration of different sensor arrays.
A Comparative Evaluation of AMQP, MQTT and HTTP Protocols Using Real-Time Public Smart City Data

MQTT, AMQP and HTTP are messaging protocols that are commonly used for communicating with resource-constrained IoT devices. HTTP is the standard reference protocol for the REST transportation based on the request/response model, whereas both AMQP and MQTT are message-oriented protocols that use the publish/subscribe model. Message-oriented protocols enhance some of the shortcomings of the complex HTTP protocol by using asynchronous communication, changing the design from a document-centric to a data-centric approach and decreasing the header and message sizes. Although significant technical detail is present on these protocols, their real-time performance is insufficiently elaborated. In this paper, we present an experimental evaluation of these protocols conducted in a homogeneous IoT testbed using a real-time Smart City public data set. We provide the behavioral differences between messaging-based protocols and the REST-based HTTP protocol in terms of message latency and CPU usage for varying traffic loads and message sizes. The results showed that MQTT and AMQP are four times faster than HTTP protocol when comparing the message sent latencies. HTTP uses four times more CPU than the AMQP and MQTT protocols. In summary, message-oriented protocols give more stable and improved results as compared to the REST model-based HTTP protocol for all evaluation scenarios.