Miracle-dz/UniFAR-FTUs huggingface dataset:
Forest Degradation Derived by a Newly Developed Sentinel-1 Change Detection Approach

Forest degradation often surpasses the area affected by deforestation and is therefore important under REDD+ monitoring, reporting and verification (MRV). Many studies dealing with forest degradation monitoring require the collection of sample data for algorithm training. The newly developed synthetic aperture radar (SAR)-based Forest Canopy Disturbance Monitoring (FCDM-radar) approach, implemented in Google Earth Engine and processing Copernicus Sentinel-1 data, allows the operational monitoring of evergreen and deciduous forest types without the need of collecting training data for calibration purposes. With a spatial resolution of 10 m it is able to detect small-scale forest canopy disturbance events such as single tree removals of 0.03 ha. Within a study area in central Cambodia (Prey Lang Wildlife Sanctuary), covering evergreen to semi-deciduous forests which are strongly affected by illegal selective logging activities, small-scale forest disturbances were successfully detected with an overall accuracy of 96.3% (user accuracy of 94.0%; producer accuracy of 79.1%).
Predicting the Depression Level of Excessive Use of Mobile Phone: Decision Tree and Linear Regression Algorithm

In this study, we apply advanced machine learning and regression analysis to find out the depression level. The use of extreme mobile phones will impact human behavior. Depression prediction is the main factor in our research. The depressive brain emits cortisol, cytokines, and serotonin; these three components are most important for the brain to maintain emotion and behavior. We measure the depression level using machine learning and data mining algorithms. We have done the whole work in the research area of medical science and information technology and also built up a collaboration. In this study, we focus on the strength of the algorithm and calculate the accuracy with python programming. The result expresses that smart mobile devices change the human brain day by day if they spend more time around 10 to 13 hours a day. Finally, we observed that a man or woman is slowly going through a depression for the impact of the excessive mobile operates. In our study, a strength calculation method is a novel approach to finding out depression level using Probability, Linear regression, Decision tree, and Naive Bayes. For the accuracy of our work, we have used three types of algorithms to find the optimal ratio and percentage
DKG-PIPD: A Novel Method About Building Deep Knowledge Graph

In this study, a novel method about building Deep Knowledge Graph for the Plant Insect Pest and Disease, namely DKG-PIPD, was proposed. Specifically, the semi-automatic extraction of semi-structured and unstructured knowledge was carried out on the basis of domain ontology, and the knowledge graph was stored in the third-party knowledge database according to the corpus characteristic of the plant insect pest and disease, to realize the visual display of entity interactive relationship and knowledge inference. Furthermore, DKG-PIPD performed joint extraction about the entity and the relationship in unstructured knowledge in a corpus tagging method that is suitable for domain data. In this way, the entity and the relationship were annotated synchronically, and the triplet can be obtained directly through label matching and label mapping, which not only effectively improved the annotation efficiency, but also solved the problem of one-versus-many overlapping relation extraction. In addition, DKG-PIPD used a novel end-to-end model to train and predict on the crawled dataset. The experimental contrast results with other classical benchmark methods demonstrated the effectiveness of the proposed method. Moreover, the related work in this paper first introduced the general architecture required for the building of knowledge graph, and then summarized its key points, that is, named entity recognition, entity relationship extraction and knowledge inference using deep learning are emphatically introduced. Finally, the improvement direction of this paper was also introduced in the discussion section.
DDIPNet and DDIPNet+: Discriminant Deep Image Prior Networks for Remote Sensing Image Classification

Research on remote sensing image classification significantly impacts essential human routine tasks such as urban planning and agriculture. Nowadays, the rapid advance in technology and the availability of many high-quality remote sensing images create a demand for reliable automation methods. The current paper proposes two novel deep learning-based architectures for image classification purposes, i.e., the Discriminant Deep Image Prior Network and the Discriminant Deep Image Prior Network+, which combine Deep Image Prior and Triplet Networks learning strategies. Experiments conducted over three well-known public remote sensing image datasets achieved state-of-the-art results, evidencing the effectiveness of using deep image priors for remote sensing image classification.
PeriodNet: A Non-Autoregressive Raw Waveform Generative Model With a Structure Separating Periodic and Aperiodic Components

This paper presents PeriodNet, a non-autoregressive (non-AR) waveform generative model with a new model structure for modeling periodic and aperiodic components in speech waveforms. Non-AR raw waveform generative models have enabled the fast generation of high-quality waveforms. However, the variations of waveforms that these models can reconstruct are limited by training data. In addition, typical non-AR models reconstruct a speech waveform from a single Gaussian input despite the mixture of periodic and aperiodic signals in speech. These may significantly affect the waveform generation process in some applications such as singing voice synthesis systems, which require reproducing accurate pitch and natural sounds with less periodicity, including husky and breath sounds. PeriodNet uses a parallel or series model structure to model a speech waveform to tackle these problems. Two sub-generators connected in parallel or in series take an explicit periodic and aperiodic signal (sine wave and Gaussian noise) as an input. Since PeriodNet models periodic and aperiodic components by focusing on whether these input signals are autocorrelated or not, it does not require external periodic/aperiodic decomposition during training. Experimental results show that our proposed structure improves the naturalness of generated waveforms. We also show that speech waveforms with a pitch outside of the training data range can be generated with more naturalness.
Biophysical Parameter Estimation Using Earth Observation Data in a Multi-Sensor Data Fusion Approach: CycleGAN

Water management and up-to-date soil moisture (SM) information are crucial to ensure agricultural activities in dry-land farming regions. In this context, remote sensing imagery coupled with machine learning techniques can provide large scale SM information if there is enough data for training, which is really limited in reality. In this paper, we explored the potential of cycle-consistent Generative Adversarial Network (GAN) for data augmentation for training machine learning algorithms, which try to model spatial and temporal dependencies between the SM prediction (output) and the remote sensing imagery (input features). Specifically, the freely available SAR (Sentinel-1) and optical (Sentinel-2) time series data were evaluated together to predict SM using GANs. The experiments demonstrate that the proposed methodology outperforms the compared state-of-the-art methods if there is not enough data to train a regression convolutional neural networks (CNN) to predict SM content.
Tuning Parameter Selection for Sentinel-2 Sharpening Using Wald's Protocol

In recent years numerous model-based methods for super-resolution of Sentinel-2 (S2) multispectral images have been suggested. Super-resolution aims to enhance the resolution of a captured image by upscaling and enhancing the details. The performance of model-based methods relies on carefully selecting regularizers and tuning parameters. This paper investigates whether using Wald's protocol, i.e., selecting tuning parameters at reduced-resolution, translates to a good performance at a full-scale. To investigate this, we use the recently proposed S2Sharp method and show that selecting its tuning parameters using Wald's protocol improves its performance.
Test Data Generation for Dynamic Unit Test in Java Language using Genetic Algorithm

Random test data generators are among the most widely used tools to generate input data for the tests. However, the data types and parameters have to be manually tailored into the tools and need to be updated manually once the source code or the test cases are changed. It is a costly process and takes a lot of time and effort to generate and update these data. Various test data generator tools are available, such as random test data generators, symbolic evaluators, and function minimization methods. In recent years some more advanced heuristic search techniques have been applied to software testing. In this study, we propose a model which automates the test data generation process. It significantly reduces the time required to generate the input data. At the same time, the data generated by our model outperforms the data generated randomly in terms of the accuracy and sensibility of the input data. It is based on the most widely used heuristic algorithm, the genetic algorithm (GA). We run the model on a sample class with six independent public methods of the different method signature, return type, and several arguments. It takes 5 seconds to generate ten possible inputs for each method with a mean, standard deviation of 0.15 and best candidate fitness average of 8.82, and means fitness of 9.79.
Post-Disaster Classification of Building Damage Using Transfer Learning

Building damage assessment after natural disasters is an important task for disaster managers and practitioners. In order to provide detailed levels of post-event building damage, this paper applies deep learning models for building localization and damage classification using transfer learning with an online free xBD dataset. The model is pretrained with ImageNet dataset. SE-ResNeXt-50-32x4d is applied for building localization, and HRNet is applied for damage classification. The building damage is divided into four levels, including no damage, minor damage, major damage, and total damage. The results show that the method can be applied for classifying building damage in an acceptable manner. This can help the government and rescue teams make disaster response quickly and support disaster management.
Exploring More Capacity for Grayscale-Invariance Reversible Data Hiding

Grayscale-invariant reversible data hiding (RDH) in color images was developed recently in which the graysclaes of the marked color image are required to be the same as that of the host color image. The grayscale-invariant RDH technique is very useful for many applications on color images in which the image processing algorithms are designed based on the gray version of the input color image. Some state-of-the-art grayscale-invariant RDH schemes were proposed recently. However their performance on embedding capacity and distortion is not satisfactory. For these existing works, the main bottleneck is the large number of correction bits generated for losslessly recovering the host green scales. In this work, we propose a novel grayscale-invariant RDH method which is able to reduce the need for correction bits. Our main contribution is to design a self-correcting mechanism which can modify the green scales for keeping grayscales unchanged while remembering the information of the host green scales. In order to obtain this task, we first observe that all previous methods generated correction bits in a functional approach which mainly results in a lot of correction bits. Instead of the functional approach, we use a relational approach to preserve the grayscales when embedding message bits. By this relational approach, the proposed algorithm only has to generate a small number of correction bits for losslessly recovering green scales. Moreover, this relational approach allows the proposed algorithm to embed more message bits into some selected green scales. The experimental results show that the proposed method significantly improves the embedding capacity and the rate-distortion performance compared to previous works.
Evaluation and Comparison of Snow Reflectance Models

The inversion of snow properties with optical remote sensing often relies on snow reflectance modelling. In this study, snow reflectance models including Mie-DISORT, Mie-Mishchenko and ART and the model based on bicontinuous microstructure are evaluated and compared with in situ measurements. Results show the bicontinuous microstructure based reflectance model can accurately simulate spectral albedo of clean and sooty snow, and have best performance on directional reflectance of snow surface compared with other three models.
Machine Learning Matching of Sentinel-2 and GPS Combine Harvester Data to Estimate Within-Field Wheat Grain Yield

This study aimed to find the most suitable data combination and estimation model of within-field durum wheat (Triticum durum) grain yield using Sentinel-2. The study was conducted in Spain, as one of the top European producers. Within-field grain yield data was obtained from a GPS combine harvester machine for 7 fields in 2018, which were consecutively processed to match Sentinel-2 10 m pixel size. Vegetation indices NDVI and GNDVI as well as biophysical parameters LAI and FAPAR were calculated from Sentinel-2 bands using the SNAP Sentinel2 ToolBox. Besides those, the Sentinel-2 10 m resolution spectral bands were used as variables for modeling, including multilinear, random forest and support vector machine regression. Various combinations of variables were tested to find the most suitable training dataset. At validation, the combination of LAI, FAPAR and Sentinel-2 10 m bands were found to be the most suitable $(\mathrm{R}^{2}=0.77$ and $\text{RSME}=1.01\mathrm{t}/\text{ha})$). The model developed with vegetation indices alone yielded the lowest $\mathrm{R}^{2}$ and highest RMSE (0.54 and 1.44 t/ha respectively). When compared to other models with the same training dataset, RF outperformed support vector machine $(\mathrm{R}^{2}=\overline{0}.73$ and $\text{RMSE}=1.11\ \mathrm{t}/\text{ha})$ and multilinear regressions $(\mathrm{R}^{2}=0.65$ and $\text{RMSE }=1.26\mathrm{t}/\text{ha})$. Hence, this study showed the efficiency of RF and Sentinel-2 data to estimate within-field wheat grain yield by using 10 m bands and biophysical parameters to train the model.
Estimating Local-Scale Groundwater Withdrawals Using Integrated Remote Sensing Products and Deep Learning

Groundwater plays a critical role in the water- food-energy nexus and extensively supports global drinking water and food production. Despite the pressing demands for groundwater resources, groundwater withdrawals are not actively monitored in most regions. Thus, reliable methods are required to estimate withdrawals at local scales suitable for implementing sustainable groundwater management practices. Here, we combine publicly available remote sensing datasets into a deep learning framework for estimating groundwater withdrawals at high resolution (5 km) over the states of Arizona and Kansas in the USA. We compare ensemble machine learning and deep learning algorithms using groundwater pumping data from 2002–2019. Our research shows promising results in sub-humid and semi-arid (Kansas) and arid (Arizona) regions, which demonstrates the robustness and extensibility of this integrated approach. The success of this method indicates that we can effectively and accurately estimate local-scale groundwater withdrawals under different climatic conditions and aquifer properties.
Design and Implementation of 5G e-Health Systems: Technologies, Use Cases, and Future Challenges

The fifth generation (5G) aims to connect massive amounts of devices with higher reliability, lower latency, and faster transmission speed, which are vital for implementing e-health systems. However, the current efforts on 5G e-health systems are still not enough to accomplish its full blueprint. In this article, we first discuss the related technologies from the physical layer, upper layer, and cross-layer perspectives on designing 5G e-health systems. We then elaborate two use cases according to our implementations (i.e., 5G e-health systems for remote health and 5G e-health systems for Covid-19 pandemic containment). We finally envision the future research trends and challenges of 5G e-health systems.
Iterative Spectral Distancing: A Novel Approach for Extracting Endmembers in Complex Urban Image Scenes

Optical remote sensing images of cities exhibit strong spectral variability, and characterising it remains a key challenge. Imaging spectroscopy is useful for this purpose, yet traditional endmember extraction algorithms are poorly suited for this type of imagery. Important issues include the non-spatiality, randomness and poor computational efficiency of existing methods, as well as the need for predefining the number of endmembers. We propose a novel algorithm, called Iterative Spectral Distancing addressing each of these issues. We show that ISD outperforms three established methods on a synthetic image, indicating its potential.
Emulation of Sun-Induced Fluorescence from Radiance Data Recorded by the HyPlant Airborne Imaging Spectrometer

The retrieval of sun-induced fluorescence (SIF) from hyperspectral radiance data grew to maturity with research activities around the FLuorescence EXplorer satellite mission FLEX, yet the used methods are computationally expensive. To bypass this computational load, this work aims to approximate the currently used spectral fitting method (SFM) by means of statistical learning, i.e. emulation. To do so, we analyzed the possibility of approximating the SFM with an emulator without losing the precision of the original method. In order to enable emulating the hyperspectral radiance spectrum into the multispectral SIF output signal, a double principal component analysis (PCA) dimensionality reduction, i.e. in both input and output, has been implemented. We systematically tested different machine learning regression algorithms, number of principal components (PCs), number of training samples and quality of training samples. The best performing emulator was then applied to a HyPlant flight line containing at sensor radiance information, and the results were compared to the SFM SIF map of the same flight line, which was used as reference. The emulated SIF map was generated quasi-instantaneously and a good agreement with the SFM map could be achieved: R2 of 0.88 and NRMSE of 3.77%. Finally, to evaluate the robustness and transferability, the emulator was applied to other HyPlant flight lines, leading to R2 of 0.97 and NRMSE of 2.56%. Generated emulated SIF maps proved to be consistent while processing time was in the order of 3 minutes. In comparison, by using SFM the SIF processing took approximately 78 minutes.
Highly Reliable Metro-Access Network Based on a Dual-Fiber Ring Architecture and Optimized Protection Mechanisms

Network reliability is very important for the next generation of optical networks. In this paper, a highly reliable protection mechanism, which integrates the approaches of optical protection and microwave protection, is proposed for metro-access optical networks based on the wavelength division multiplexing (WDM) technique. By designing a scheme for the convergence of optical and wireless access and cooperating with designed remote nodes (RNs), not only can access for both wired and wireless service be provided simultaneously in each optical network unit (ONU) but also microwave protection can be realized when a failure occurs in a distribution segment. By developing a novel optical switch module (OSM) to cooperate with the dual-fiber ring structure, flexible switching can be achieved to protect against multiple types of failures in the feeder segment, including single-fiber faults, dual-fiber faults and multifiber faults. Based on the above structure, the influence of Rayleigh backscattering (RB) noise can be mitigated when the network is operating under all operating scenarios except for an inner-fiber fault. To further mitigate the influence of noise, optimization for upstream transmission performance is implemented by configuring a reflective semiconductor optical amplifier (RSOA). Furthermore, the feasibility of the overall scheme is verified through experiments and simulations under different operating conditions. The results show that the proposed mechanisms can achieve acceptable bit error rate performance and quality of service (QoS) metrics while also offering various advantages, including high reliability, large scale, and a relatively low cost.
RobHand: A Hand Exoskeleton With Real-Time EMG-Driven Embedded Control. Quantifying Hand Gesture Recognition Delays for Bilateral Rehabilitation

Assisted bilateral rehabilitation has been proven to help patients improve their paretic limb ability and promote motor recovery, especially in upper limbs, after suffering a cerebrovascular accident (ACV). Robotic-assisted bilateral rehabilitation based on sEMG-driven control has been previously addressed in other studies to improve hand mobility; however, low-cost embedded solutions for the real-time bio-cooperative control of robotic rehabilitation platforms are lacking. This paper presents the RobHand (Robot for Hand Rehabilitation) system, which is an exoskeleton that supports EMG-driven assisted bilateral by using a custom-made low-cost EMG real-time embedded solution. A threshold non-pattern recognition EMG-driven control for RobHand has been developed, and it detects hand gestures of the healthy hand and replicates the gesture on the exoskeleton placed on the paretic hand. A preliminary study with ten healthy subjects is conducted to evaluate the performance in reliability, tracking accuracy and response time of the proposed EMG-driven control strategy using the EMG real-time embedded solution, and the findings could be extrapolated to stroke patients. A systematic review has been carried out to compare the results of the study, which present a 97% of overall accuracy for the detection of hand gestures and indicate the adequate time responsiveness of the system.
Deep learning-based non-intrusive detection of instabilities in formulated liquids

Stability is a key property of formulated liquids for industrial applications. Before their commercialization, a solid assessment of stability under various operative conditions must be carried out. Traditionally, this is performed by expert researchers that observe the liquid over time and point out the possible occurrence of instabilities. However, this is a costly and time-consuming process. Here, we investigate the potential of deep learning approaches for automatic image-based assessment of formulated liquid stability. Leveraging a recently developed dataset, comprising thousands of images of formulated liquids stored in transparent jars, we implement and test several state-of-the-art Convolutional Neural Networks (CNNs) with different loss functions and augmentation strategies. Experiments prove the effectiveness of this non-invasive approach opening the way to further applications.