Miracle-dz/UniFAR-FTUs huggingface dataset:
Enchancing SLAM method for mapping and tracking using a low cost laser scanner

This paper presents a SLAM technique that does not use odometer information. It is based on HECTOR SLAM method from Technische Universitat of Darmstadt, but using a different hardware from the proposed and finally without the use of IMU device. The method is based on modified settings of the HECTOR SLAM method and manages to optimize the method based on COTS hardware.
Post-ICU Care: A Qualitative Analysis of Patient Priorities and Implications for Redesign.

RATIONALE While survival during critical illness is improving, little evidence exists to guide post-intensive care unit (ICU) care. Understanding patients' needs and priorities is fundamental to improving care quality. OBJECTIVE To describe the evolution of patients' priorities for recovery across the spectrum of post-ICU care. METHODS This was a secondary analysis of 39 semi-structured interviews conducted from 2005-2006, in participants' homes 19 days to 11 years after hospital discharge post-critical illness. Adult critical illness survivors (n=39) ≥ 20 years old from multiple ICUs across the United Kingdom were purposively selected to maximize diversity with respect to time since diagnosis, disease severity, gender, age, ethnicity, socio-economic group/status, region. age, ICU admitting diagnoses, and length of stay. We used the method of qualitative description to characterize patients' priorities for recovery and their evolution within and between individual patients, across three post-ICU periods: ICU transition to wards, early period (approximately the first 2 months) after discharge home, and late period (>2 months) after discharge home. RESULTS The analysis revealed 12 core patient priorities during recovery: feeling safe, being comfortable, engaging in mobility, participating in self-care, asserting personhood, connecting with people, ensuring family well-being, going home, restoring psychological health, restoring physical health, resuming previous roles and routines, and seeking new life experiences. In general, priorities evolved from those pertaining to basic survival during the stay on wards to being broader and more aspirational by the late post-discharge period. CONCLUSIONS Understanding patients' priorities for post-ICU care is critical for developing stakeholder-driven clinical guidelines. Engaging other stakeholders (e.g., family members, healthcare providers, institutionalized and frail older adults) to inform the development of clinical guidelines for post-ICU care, together with the barriers and facilitators faced in achieving patient- and family-centered care, is an important next step.
Sa-Bilasar Down-Looking 3-D Imaging Based on Sparse Bayesian Reconstruction

Spaceborne airborne bistatic linear array synthetic aperture radar (SA-BiLASAR) down-looking imaging is a novel and promising three-dimensional (3-D) radar imaging technique. To improve the imaging quality and resolution, a sparse imaging method is proposed for SA-BiLASAR down-looking 3-D imaging based on the sparse Bayesian reconstruction (SBR) theory. The bistatic geometric model and the echo model of SA-BiLASAR down-looking sparse imaging are derived. Then, a sparse expression of target by Laplace distribution is exploited, and an iterative optimization estimation method is applied for sparse target reconstruction. In addition, to correct the geometric distortion of 3-D image due to the bistatic observed mode of SA-BiLASAR, a geometric correction method is presented. Numerical simulation results demonstrate the effectiveness of the presented SBR method for SA-BiLASAR down-looking high-resolution 3-D sparse imaging.
New Neural Network and an Image Postprocessing Method for High Resolution Satellite Imagery Road Extraction

Recently, D-LinkNet has become a popular convolutional neural network for its high IoU scores in road extraction. Actually, D-LinkNet could have higher IoU scores if it uses ResNet that has deeper network as its encoder part, but the scale of the model would be very huge because of the center part design called DBlock in this paper. Focused on the problem, this paper made some improvements: (1) Add a 1 × 1 convolution that can be introduced as bottleneck layer before DBlock to reduce the number of input feature-maps, so as reduce the parameters and improve computational efficiency. Then add another 1 × 1 convolution after DBlock to recover the required output channels; (2) Rebuild the entire network based on ResNet units with new structure instead of the original one. The new network named as D-LinkNetPlus. Except for reducing parameters, this paper also proposed a method named ESIPs to eliminate small independent patches for the accuracy improvement of road extraction. The experimental results show that the D-LinkNetPlus not only has less parameters but also has a higher accuracy than the results gotten by original structure of D-LinkNet. In addition, the road extraction results presented a better visual effect after processing by ESIPs proposed in the paper.
Image Recognition Method Based on an Improved Convolutional Neural Network to Detect Impurities in Wheat

Impurities in wheat seriously affect wheat quality and food security. They are mainly produced during the operational process of combine harvesters. To solve the recognition and classification problems associated with impurities in wheat, a recognition method using an improved convolutional neural network is proposed in this article. A labeled dataset of normal wheat and five impurities is constructed, using which the Wiener filtering algorithm and the multi-scale Retinex enhancement algorithm are employed for image preprocessing. Based on network research using Inception_v3, improvement and optimization are undertaken before designing the WheNet convolutional neural network, which is intended for automatic recognition of wheat images. Under the same conditions, comparative experiments using the WheNet, ResNet_101 and Inception_v3 networks are conducted. Indexes such as receiver operating characteristic, area under curve (AUC), and recall rate are adopted to evaluate the experimental outcomes. Experimental results indicate that the WheNet network achieved the most efficient results. It also shows a shorter training time, and its recognition accuracies for Top_1 and Top_5 of the test set are 98.59% and 99.98%, respectively. The mean values of both the AUC and recall rate of the network on the recognition of various images of impurities are higher than those of the ResNet_101 and Inception_v3 networks. Consequently, the WheNet network can be a useful tool in recognizing impurities in wheat. Furthermore, this method can be used to detect impurities in other fields.
Research Progress of Satellite Altimeter Calibration in China

In this paper, the research progress and its application of the Chinese calibration sites for satellite altimeters are described. The geodetic surveying and the surface subsidence monitoring over the Qianliyan calibration site using a permanent GNSS station are described. The new calibration results for HY-2A, Jason-2&3, Saral, and Sentinel-3A are presented in detail. Then, the Wanshan sites which is still under construction in the southern Chinese coastal area are introduced. In addition, the wet delay of troposphere measured by Jason-2 AMR in 2010-2016 is evaluated through one site from the Chinese coastal GNSS network, which proves the feasibility of calibrating microwave radiometer wet delay through the Chinese coastal GNSS network.
Full-Resolution Image Segmentation Model Combining Multi-Source Input Information

In this paper, a full-resolution image segmentation model with multi-source input information is proposed and applied to road extraction. The convolution-deconvolution network is adopted as the backbone network, and a full resolution network branch is added into the backbone network. A data exchange mechanism is established between the backbone network and the full resolution branch, which not only overcomes the problems of reduced feature resolution and loss of detailed information caused by repeated pooling operations, but also aggregates multi-scale features in convolution stage. The aggregated features are transferred to the corresponding layers in deconvolution stage, which enhances the feature fusion. Multi-source images are used as input, and the predictions are fused by weighting at the end of the network to highlight the target while effectively suppressing the misclassification. Experiments on Road Detection Dataset show that the results of the proposed method are superior to those of state-of-the-art comparison methods.
Epileptic EEG Signal Classification using Multi-class Convolutional Neural Network

Epileptic seizure is a common neurological syndrome wherein patients suffer from repeating seizures. It happens due to many provoking reasons namely genetic, physiologic, brain damage, etc. In this paper, time domain and frequency domain feature set are derived from EEG data and classified using Convolutional Neural Network (CNN) for detecting epileptic seizures. The benchmark EEG dataset Bonn is experimented to compare and examine the result of forthcoming methods. The proposed methodologies have been evaluated using the metrics such as sensitivity, specificity, classification accuracy and execution using two different benchmark datasets and results are compared with exiting methods. It is learned that among various methods the proposed approach using convolutional neural network with time domain features provides enhanced performance measured result. As the time domain features are computationally inexpensive to extract and also required reduced storage, so it can be used in real time automatic seizure detection and monitoring using wireless systems.
Adaptive Fuzzy Control Method for a Single Tilt Tricopter

This article proposes an adaptive fuzzy gain scheduling (FSG) design of the traditional proportional integral derivative (PID) control method by using fuzzy logic rules to schedule controlled gains at different phases. Owing to minimization of the tracking error of the controller design using three parameters and the integral of time weighted-squared error (ITSE) minima criterion of the controller design process, the fuzzy rules of the triangular membership functions are exploited online to verify the PID controller gains in different operated scheduling modes. For that reason, the controller designs can be used to tune the system models during the whole operation time period to enable efficient error tracking. The continuous genetic algorithm (GA) is considered an innovation because in it, the decode chromosome step is totally neglected. Owing to this improvement, it is superior to the standard GA because it requires less storage and enables naturally faster convergence. In this research, the controlled parameters were optimized using the continuous GA to enhance the efficiency of the proposed method. Thereafter, it was implemented to a single tilt Tricopter model to test whether the control performance is better when compared with the conventional PID control method.
A Spectral Mixing Model Accounting for Multiple Reflections and Shadow

In this work, we present a nonlinear spectral mixing model that, apart from the fractional abundances, contains two additional parameters, one accounting for multiple reflections and another accounting for shadow. The model is based on the multilinear mixing (MLM) model that we have proposed earlier. An analysis of the parameter values is performed on a close-range hyperspectral image of a building facade. The model is compared to the linear model and two models that account for only one of the two effects: the linear model with an extra shadow endmember, and the MLM model.
Stablization of an Underactuated Surface Vessel with Disturbances

The stabilization of an underactuated surface vessel is studied. Environmental disturbances are taken into account. By using global diffeomorphism transformation, the plant is transformed to an equivalent cascaded system. By defining a Lyapunov function and introducing two auxiliary controllers, a robust feedback control strategy is proposed to stabilize the obtained equivalent system. Simulation is conducted to verify the proposed stabilization controller.
Density Peak Based Covariance Matrix for Hyperspectral Images Classification

The clustering methods have a good application in many aspects, in which the density peak (DP) clustering can effectively cluster similar neighboring pixels, so that the features can be extracted well for hyperspectral images (HSIs) classification. In this work, a density peak based covariance matrix (DPCM) method is proposed for HSIs classification, which not only can effectively extracts features, but also can reduce the within-class variations and the between-class interference. The proposed method consists of the following steps: first, maximum noise fraction (MNF) is employed on the original HSI to reduce the computational complexity and eliminate noise. Second, the local densities of sample is calculated by the DP clustering. Therefore, the density map can be obtained in which each pixel has a density value in the original image. Then, the covariance matrix between each density pixel in the density map is calculated. Last, the extracted covariance matrices are fed back to the support vector machine (SVM) based on the logarithm Euclidean kernel for label assignment. Experiments on the Indian pine data set show that this method is superior to other classification methods.
Multi-objective Optimization of C4.5 Decision Tree for Predicting Student Academic Performance

Applying data mining methods in the educational field has gained a lot of attention among scientists over the last years. Educational Data Mining forms an ever-developing research area aiming to unveil the hidden knowledge in educational data and improve students’ learning behavior and outcomes. To this end, a plethora of data mining methods have already been implemented in various educational settings solving a variety of tasks, among which the prediction of students’ academic performance as well. Decision trees have proven to be a quite effective method for both classification and regression problems showing a number of considerable advantages, such as efficiency, simplicity, flexibility and interpretability. Moreover, configuration of parameter values has often a material impact on building optimal trees in terms of accuracy and/or size. In this context, the main objective of our study is to yield a highly accurate and interpretable classification tree for the early prognosis of students at risk of failing in a university course. Thereby, effective intervention and support actions could be initiated to motivate students and enhance their performance. The experimental results demonstrate that the induction of the C4.5 decision tree classifier through an evolutionary algorithm, such as the Speed -constrained Multi-objective Particle Swarm Optimization algorithm, yields more accurate and easier to construe trees.
An Improved Low-Noise Sensorless PMSM Drive able to Face Highly Intermittent Load Torque

Sensorless algorithms for Permanent Magnet Synchronous Motor (PMSM) are generally divided in methods for the low speed region and methods for the medium-high speed region. Algorithms based on high frequency injection show a good behavior for low motor speed, nevertheless voltage injection is associated to loud acoustic noise and additional losses. On the contrary, methods based on back-electromotive force and on rotor flux observer are passive methods (i.e. they do not require additional voltage injection); however they guarantees high performance only in the medium-high speed region. In this paper, rotor flux observer based sensorless algorithm performance is analyzed, specially focusing on behavior at low speed. No-load, linear torque, quadratic torque and compressor-type torque load is experimentally tested.
Forest Monitoring in Guatemala Using Satellite Imagery and Deep Learning

Forests cover 34% of Guatemala. The Guatemalan government have taken proficient actions in past decades to reduce deforestation and are looking toward new space technologies to improve forestry monitoring. This paper demonstrates the ability to automatically detect pixel-level changes in satellite images of forested areas that can be used to assist Guatemalan agencies, using satellite imagery from the Copernicus program and specially-developed deep learning algorithms for image segmentation.
Trigraph Regularized Collective Matrix Tri-Factorization Framework on Multiview Features for Multilabel Image Annotation

Due to the explosive growth of image data, image annotation has been one of the most popular research directions in computer vision. It has been widely used in image retrieval, image analysis and understanding. Because traditional manual image annotation is time consuming, more advanced automatic annotation methods are needed. A major challenge in developing an efficient image annotation method is how to effectively use all available information contained in the data. To this end, this paper proposes a novel image annotation framework that uses multiple information from data. It employs nonnegative matrix tri-factorization (NMTF) to simultaneously factorize image-to-label, image-to-feature, and feature-to-label relation matrices using their intertype relationships and incorporates the intratype information through manifold regularizations. This method can be referred to as the trigraph regularized collective matrix tri-factorization framework (TG-CMTF). TG-CMTF captures the correlations among different labels, different images and different features. By taking advantage of these relations from images, features and labels, TG-CMTF can achieve better annotation performance than most state-of-the-art methods. The promising experimental results on three standard benchmarks have shown the effectiveness of this information. Furthermore, we show the annotation process as a precise optimization problem and solve it by an iterative algorithm, which proves the correctness of the proposed method from the mathematical theory.
The Utilization of Multi-Label Samples For Hyperspectral Image Classification

The number and quality of training samples have a big influence on hyperspectral image classification performance. However, it is often difficult to manually annotate a large number of accurate training samples because the annotation requires a lot of manpower and resources. In this paper, we first propose a multi-labeling method to label the training samples efficiently. Instead of giving the exact label for each training pixel, we just precisely label a small number of pixels (called single-label samples), and annotate a large number of pixels in certain regions together (called multi-label samples) with multiple labels. Furthermore, a superpixel segmentation and recursive filtering based sample enhancing method is proposed to make full use of multi-label training samples for classification, which consists of the following major steps: IFRF based feature extraction, superpixels based classification, and spatial-spectral similarity based inaccurate samples removal. Experimental results demonstrate that the proposed method can improve the classification accuracy of multiple classifiers with multi-label training samples.
Fast Loop Closure Detection via Binary Content

Loop closure detection plays an important role in reducing localization drift in Simultaneous Localization And Mapping (SLAM). It aims to find repetitive scenes from historical data to reset localization. To tackle the loop closure problem, existing methods often leverage on the matching of visual features, which achieve good accuracy but require high computational resources. However, feature point based methods ignore the patterns of image, i.e., the shape of the objects as well as the distribution of objects in an image. It is believed that this information is usually unique for a scene and can be utilized to improve the performance of traditional loop closure detection methods. In this paper we leverage and compress the information into a binary image to accelerate an existing fast loop closure detection method via binary content. The proposed method can greatly reduce the computational cost without sacrificing recall rate. It consists of three parts: binary content construction, fast image retrieval and precise loop closure detection. No offline training is required. Our method is compared with the state-of-the-art loop closure detection methods and the results show that it outperforms the traditional methods at both recall rate and speed.
Implementing a MOOC course for Museum Professionals with a worldwide effect

It has been observed that the fast pace of the adoption of ICT in the museum sector concluded to a detachement between formal education or training and the labor market. Museum professionals need to acquire new skills and competences in order to deal with the digital challenges in their everyday work life. The adoption of new technologies and the continuous vocational development for the scientific personnel of a museum is considered a great asset. For this reason, the goal of Mu.SA (Museum Sector Alliance) project is to underline and deal with the lack of digital and transferable competences in the sector [1]. In this paper, will be presented the aim, the action plan, the main outputs and the first research results of Mu.SA, a Sector Skills Alliances EU funding project. The Mu.SA project develops an updated training programme adapted to the needs of museum professionals based on surveys and interviews conducted within the project across Europe. Also, it produces a range of innovative outcomes, including European profiles of emerging job roles in museums that serve as a common reference at European level, a staged VET methodology based on learning outcomes, policies and tools for assessment and validation of non-formal/ informal learning as well as modular VET curricula. The MOOC delivered in the first trimester of year 2019 raised the interest of participated museum professionals from all over the world