Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomous Mapping and Spraying in Precision Agriculture using Unmanned Ground Vehicles

Currently, unmanned systems are seeing rapidly and extensive integration into several businesses, encompassing sectors such as healthcare and transportation. In the field of precision agriculture, a range of unmanned systems, including mobile robots, Unmanned Aerial Vehicles (UAVs), and Unmanned Ground Vehicles (UGVs), are employed. This paper presents the concept of a completely autonomous vehicle capable of detecting vegetation, navigating around obstacles, determining optimal routes, and executing independent movement. The utilization of a stereo camera is imperative for the execution of various processes, encompassing the important mapping process. All processes, including the mapping process, are conducted with a stereo camera model. This research centers around an autonomous ground vehicle designed to perform mapping and agricultural spraying tasks. Notably, the vehicle is developed to fulfill its primary function without relying on 3D LIDAR or GPS technology. Within the scope of this investigation, a model is trained using the original dataset, which comprises two distinct classes of yolov7: wild and cultivated plants. The training process yields a model that exhibits a precision rate of 99.6%. Furthermore, the identification of obstacles is accomplished through the utilization of depth information obtained from the camera system installed on the vehicle. By utilizing the IMU sensor included within the camera and incorporating depth information, a two-dimensional array is employed to replicate the surrounding world. Through the examination of the incoming data pertaining to the simulated environment, as well as the positions of vehicles and obstacles, it becomes feasible to determine the appropriate placement of vegetation and agricultural products, whether they have been subjected to spraying or not. Ultimately, the autonomous determination of the vehicle’s probable route can be determined.
End-to-End Trainable Gaussian Filtering for Electrocardiogram Signal Classification Using Deep Learning

Deep Learning (DL) is increasingly used in electro-cardiograms (ECGs)-based signal analysis allowing for diagnosing a variety of cardiac disorders and enabling the development of automated diagnosis and prognosis systems. However, ECG signals are typically noisy requiring a series of prepossessing steps before feeding them into DL models for analysis. The main contribution of this paper is a simple, yet very effective end-to-end hybrid trainable filtering and feature extraction pipeline, that is formulated as a set of differentiable layers that can be incorporated into any DL model and employ Gaussian filters as a prior model. Indeed, it is experimentally demonstrated that the proposed method can increase the performance of ECG-based classification both for baseline architectures (e.g., MLPs, CNNs, LSTMs) as well as for state-of-the-art architectures proposed in the literature for ECG signal analysis. Given the simplicity and effectiveness of the proposed method, we believe that it constitutes a practical tool that can readily be incorporated into any DL pipeline that is used for time-series analysis, potentially further increasing its performance with minimal computational overhead.
Graphene field-effect transistors as THz detectors: Distinguishing between resistive self-mixing and the hot-carrier thermoelectric effect

Graphene-based detectors of THz radiation – despite their improvement over the years – continue to exhibit a lower sensitivity than detectors made from other material systems. In order to gain a deeper understanding of some of the detection processes and their constraints, we analyze here experimentally and by simulations the operation of graphene TeraFETs, detectors based on rectification in antenna-coupled field effect transistors, over the frequency range 0.1-1.2 THz. The devices were fabricated with an advanced wafer-scale process technology. It is well-known that two detection mechanisms contribute, resistive self-mixing and the photothermoelectric effect. We determine the respective contributions to the responsivity and their frequency dependencies, put the results into perspective with competing technologies, and suggest ways to improve detector performance.
Fully Convolutional Event-camera Voice Activity Detection Based on Event Intensity

The use of visual signals to detect vocally active duration is quite helpful when there is severe acoustic noise or even can be the only option if the audio channel is missing. There has been significant progress in video-based voice activity detection (VAD). On the other hand, while recently emerging event camera (EC) technology has demonstrated great benefits for applications in robotics, drones, autonomous vehicles, and mobile devices, including visual speech recognition topics, it has not been explored to be used as a vision-only VAD front-end. In this work, we propose an event intensity-based method by designing a fully convolutional network to efficiently realize an EC-VAD that segments vocally active duration. Efficiency is due to pooling the data over the mouth area reducing the dimensions by totally collapsing local spatial information as well as due to one-stage detection by a fully temporal convolutional network. Experimental evaluations show successful detection of voice activity with about 0.91 area under the receiver operating curve over a dataset including high speech content variability and different types of facial actions.
A Boundary Optimization Scheme for Liver Tumors from CT Images

Liver CT scan image analysis plays an important role in clinical diagnosis and treatment. Accurate segmentation of liver tumor from CT images is the prerequisite for targeted therapy and liver resection. Existing semi-automatic segmentation based on graph cuts or fully automatic segmentation methods based on deep learning have reached the level close to that of radiologists. To improve tumor segmentation on liver CT images, we propose a fully automatic post-processing scheme to optimize tumor boundaries. This method improves boundary prediction performance by optimizing a sequence of patches extracted along the initial predicted boundary. The proposed boundary refinement segmentation network obtains strong semantic information and precise location information through the information interaction between different branches, to achieve precise segmentation. The Liver Tumor Segmentation (LiTS) dataset is used to evaluate the relative segmentation performance obtaining an average dice score of 0.805 using the new method.
Nonlinear Causality Inference Using a Robust ERR-Based Method in the Context of Epilepsy

In this paper, a robust error reduction ratio based method is proposed for the estimation of nonlinear causality among dynamic systems. The proposed method copes with the issue of spurious solutions inherent to the original ERR-based method. These spurious solutions are removed by considering a sparse representation of the model coefficient vector. This sparse representation is recovered using the well-known alternating direction method of multipliers combined with an optimal computation of the regularization parameter at each iteration using the discrepancy principle. The robust ERR-based method is evaluated in the context of inferring brain effective connectivity in epilepsy. Results obtained on simulated and real intracerebral electroencephalographic signals confirm its efficiency.
An Analytical Solution to Investigate the Impact of an Insulating Layer with High Permittivity Materials in Magnetic Resonance Imaging Applications

In this work we present a new analytical framework to investigate how the presence of an insulating layer, such as the distance between a high-permittivity helmet and the body tissues can impact the field distribution in the region of interest, and the choice of the optimum permittivity to this purpose. The framework is based on a recent reformulation of the Mie scattering for a layered sphere and is validated with a numerical method based on the finite difference time domain.
A New Expected Improvement Acquisition Function for Expensive Multi-Objective Optimization

While Bayesian optimization, a popular solution to black-box optimization problems, has been extended to handle expensive multi-objective optimization problems (MOPs), expected improvement (EI), a commonly used acquisition function, for MOPs is often either computationally expensive or inefficient to balance convergence and diversity. This paper proposes a new expected improvement acquisition function for MOPs, which can take advantage of both EI and multi-objective optimization. To extend the EI to MOPs, all Pareto optimal solutions obtained so far are used to calculate the EI values, resulting in an EI matrix. Instead of transferring the matrix into a scalar value, the proposed algorithm identifies the Pareto optimal solution with the minimum sum of EI values, and utilizes the corresponding EI vector from the matrix as the final value of the acquisition function. The efficiency of the proposed acquisition function is validated by comparing it with state-of-the-art multi-objective Bayesian approaches on two widely used test suites.
Factors Influencing the Learner’s Cognitive Engagement in a Language MOOC: Feature Selection Approach

This study aims to predict the cognitive engagement rate in a Language MOOC (Massive Open Online Course) based on the features extracted from learners’ engagement behaviors within the content and activities. The features were extracted from the data of the Language MOOC “Türkçe Öğreniyorum (I learn Turkish)” which aims to provide self-paced learning materials for those interested in developing their skills in Turkish as a foreign language. After the data preprocessing processes were carried out with the data set obtained for cognitive engagement classification, feature selection processes were performed using filtering and wrapper methods. Afterward, the machine learning model trained using the Logistic Regression (LR) algorithm performed the classification with 94% accuracy. The model evaluation metrics also support the classification result obtained. Based on the extracted features and the classification results obtained, the model will be able to capture learners’ interaction behaviors with the content and activities in a Language MOOC and detect changes in learner behavior over time. Prediction accuracy is essential to offer dynamic content and activities in a Language MOOC for adjusting the individual needs of each learner, providing personalized learning experiences that are tailored to their skills, knowledge, and preferences.
The Clinical Application Value of RAPID Software for Endovascular Treatment Decision of Acute Isolated M2 Occlusion

Objective: The aim of this study is to analyze the impact of RAPID software on the efficacy and safety of Endovascular Therapy (EVT) for patients with acute isolated M2 occlusion. Methods: The patients with acute isolated M2 occlusion received endovascular treatment in our hospital, between January 2020 and April 2021 were retrospectively analyzed. Baseline characteristics and imaging parameters of CTP-RAPID were collected for analysis. The primary outcomes included favorable functional outcomes (modified Rankin Scale ≤ 3) at 90 days, and mortality within 90 days and symptomatic intracerebral hemorrhage were analyzed. Results: Among the 62 patients enrolled in this study, 48 (40.0%) achieved a good functional outcome (modified Rankin Scale score ≤ 3 at 90 days). In the univariate analysis, Alberta Stroke Program Early CT Score, perfusion deficit volume in time to maximum (Tmax) >6s, and mismatch volume were associated with functional outcomes (all p<0.05). In the multivariate analysis, infarct core (CBF<30%) remained independent outcome predictors (p<0.05). Conclusion: RAPID software based on CTP represent a promising tool for patients with acute isolated M2 occlusion to make decision and predict prognosis for endovascular treatment.
Efficiently Treated Sewage Sludge Supplemented with Nitrogen and Potassium Is a Good Fertilizer for Cereals

Sewage sludge contains phosphorus (P), nitrogen (N), potassium (K) and organic matter in both soluble and particulate forms. However, agricultural use of sludge is limited because of possible problems due to hygiene, heavy metals, inner quality variation and non‐balanced ratios of nutrients. We tested sustainable fertilization of cereals with (i) urea‐spiked granulated sludge, (ii) composted anaerobic sludge digestate, (iii) dried (dewatered), anaerobic thermophilic digestate sludge mixture and (iv) dried (dewatered) sludge. The sludge products were applied to barley and wheat with variable combinations of N and K supplements in Nordic climate. Sludge products supplemented with N and K and mineral NPK fertilizers containing soluble 70 kgN ha−1 achieved barley dry yields above 3500 kg ha−1. The grain N content was 22·3 g kg−1 in barley fertilized with granulated sludge fortified with NK (soluble N 83 kg ha−1), while grain N content was 20 g kg−1 when fertilized with 90 kgN ha−1 NPK. The wheat dry yields were 2890 and 2850 kg ha−1 fertilized with granulated sludge fortified with NK when soluble N was 49 kg ha−1 and NPK with 110 kgN ha−1 of these grains containing N 29·2 and 29·0 g kg−1. The results mean that correctly treated and formulated sludge products can be used. The risks caused by enteric microorganisms or heavy metals could be managed. The residual nitrate concentrations in soil were low. Sludge products could be developed by optimizing nutrient ratios and their structure. Sludge producers should provide farmers with detailed physicochemical data about each sludge portion. Copyright © 2016 John Wiley & Sons, Ltd.
Antarctic icebergs distributions 1992-2014: ANTARCTIC ICEBERG

Basal melting of floating ice shelves and iceberg calving constitute the two almost equal paths of freshwater flux between the Antarctic ice cap and the Southern Ocean. The largest icebergs ( 100 km2) transport most of the ice volume but their basal melting is small compared to their breaking into smaller icebergs that constitute thus the major vector of freshwater. The archives of nine altimeters have been processed to create a database of small icebergs ( 8 km2) within open water containing the positions, sizes, and volumes spanning the 1992–2014 period. The intercalibrated monthly ice volumes from the different altimeters have been merged in a homogeneous 23 year climatology. The iceberg size distribution, covering the 0.1–10,000 km2 range, estimated by combining small and large icebergs size measurements follows well a power law of slope −1.52 ± 0.32 close to the −3/2 laws observed and modeled for brittle fragmentation. The global volume of ice and its distribution between the ocean basins present a very strong interannual variability only partially explained by the number of large icebergs. Indeed, vast zones of the Southern Ocean free of large icebergs are largely populated by small iceberg drifting over thousands of kilometers. The correlation between the global small and large icebergs volumes shows that small icebergs are mainly generated by large ones breaking. Drifting and trapping by sea ice can transport small icebergs for long period and distances. Small icebergs act as an ice diffuse process along large icebergs trajectories while sea ice trapping acts as a buffer delaying melting.
Artificial Intelligence-Powered System for Detecting, Diagnosing, and Rehabilitating Strabismus Disorder

Artificial Intelligence (AI) and machine learning analyze strabismus via Electro-oculography (EOG) signals, addressing eye muscle irregularities. Detecting this disorder's manifestation in both eyes is challenging, impacting the vision and the 3-dimensional perception. To this purpose, the aim is to develop an ophthalmologist-assisting system that reduces costly interventions like eyewear, lasers, and drugs. EOG signals from electrodes on the face reveal insights, focusing on the Center of Mass (CoM) of the ocular movements. The CoM determines the ocular misalignment extent due to muscle irregularities. Using the same device, ophthalmologists guide patient recovery through gamified exercises. Results highlighted the system's efficacy in differentiating between normal and aberrant conditions, quantifying deviation, and treating strabismus. In essence, AI and machine learning with EOG signal analysis offer a transformative solution for strabismus, providing early detection, accurate classification, and tailored rehabilitation for this ocular condition. The results of this study also suggested that the K-Nearest Neighbors (KNN) and wavelet decomposition tree methods may be effective for the classification and feature extraction of EOG data in the detection and diagnosis of eye diseases. These methods showed high levels of accuracy, with 98.3 and 98.5% respectively. However, further research is needed to confirm and extend these findings, and to explore the potential of these methods in the context of EOG and eye disease detection.
Anisotropic Diffusion-based Analog CNN Architecture for Continuous EEG Monitoring

This study proposes an ultra-low-power processing approach for early seizure detection using electroencephalogram (EEG) signal processing. Traditional methods of EEG processing are power-hungry and resource-intensive, making them impractical for long-term, real-time monitoring. We propose a two-tier wireless sensor network architecture with all-analog in-situ processing at the sensor level and Cluster-Heads (CHs) to compile the data into meaningful information. The paper introduces a novel all-analog Convolutional Processing Unit (CvPU) that utilizes anisotropic diffusion properties in electrical circuits and a neural-network architecture for EEG-based seizure detection. The power consumption of the proposed architecture is estimated to be 1 to 3 orders of magnitude lower than contemporary digital and hybrid analog-digital approaches. The proposed approach is cost-effective, generates a comprehensive picture of an individual’s brain health, and can be applied in other physiological monitoring areas, including athletic fitness assessments and personal-health monitoring.
New MCI Detection Method Based on Transformer and EEG Data

Preventing health issues is a crucial aspect of the medical field, particularly when it comes to mild cognitive impairment (MCI), which is a risk factor for developing dementia. Early detection of MCI is essential, and there are two types of MCI: amnestic MCI (aMCI) and non-amnestic MCI (naMCI). However, it is challenging to differentiate between individuals with MCI and those who are aging normally. Electroencephalography (EEG) is a promising modality for diagnosing MCI, which provides information about an individual's cognitive state during a clinical examination. This research aims to distinguish between individuals diagnosed with MCI as either aMCI or naMCI, and healthy controls (HC) during a verbal fluency task (VFT). To achieve this, a new MCI detection method based on the transformer architecture was proposed. This method makes use of EEG data and achieves up to 94.78% accuracy.
Influence of atmospheric waves on the formation and the maintenance of the subtropical jet during the Northern Hemisphere winter—A new method for analyzing the responses to specific forcings

This paper introduces a new analysis method that can isolate the responses to specific forcings within meteorological data. By using the zonal mean primitive equations on the sphere, it is possible to break down the meridional circulation, the acceleration of zonal wind, temperature change, and surface pressure change into their individual contributions, which are directly associated with various forcings. This analysis technique can be applied to a wide range of problems relating to climate and its variability. To demonstrate the application of the technique, the formation and maintenance of the subtropical jet during the Northern Hemisphere winter are examined. It is found that atmospheric waves play a crucial role in both the climatological maintenance and the day‐to‐day (and month‐to‐month) variabilities of the jet. While stationary waves are the dominant catalyst for maintaining the jet in its climatological state, synoptic waves play an important role in generating the month‐to‐month variability of the jet.
Dynamic fluid recycling at Laguna Caliente (Poás, Costa Rica) before and during the 2006–ongoing phreatic eruption cycle (2005–10)

Abstract Poás Volcano (Costa Rica) resumed phreatic activity on 24 March 2006 after twelve years of quiescence. From March 2006 to June 2010, the initial phase of the ongoing eruption cycle, 110 phreatic eruptions were reported. This study presents the temporal variations in the chemical and isotopic (δD and δ18O) compositions of Laguna Caliente crater lake for the period prior to the eruption until June 2010. No systematic relationship with the phreatic eruptive activity exists. A combined mass and Cl budget analysis enables quantification of the seepage rate (7 kg s−1), the input rate of the ‘volcanic fluid’ (Qf), and the Cl concentration (Cle) in the evaporation plume (13 300 mg l−1). A modelling procedure for variable seepage rates leads to more realistic estimates of 50–100 kg s−1, which better represent the observed lake water chemistry, hence suggesting dynamic fluid recycling between the lake and the underlying magmatic–hydrothermal system. The high Cl concentration in the evaporation plume and the dynamic fluid recycling at the lake bottom characterize Laguna Caliente as an ‘open-air’ fumarole, discrediting water chemistry as an efficient monitoring tool at the classic monitoring frequency. A conceptual model of phreatic eruptions is linked to our observations.
Machine Learning Approach for Distributed Daniel of Service Attack Detection in SDNs

Software-defined networks (SDN) have attracted substantial attention as a promising network technology, providing centralized control and programmability through the SDN controller, which provides resilience for network management and topology of the infrastructure network. Hence, the security of SDN controllers remains a significant concern, where Distributed Daniel of Service (DDoS) attacks pose a threat and obstruction, causing disability for the entire network or a potential failure point for the SDN control unit. In this paper, a machine learning (ML) approach for detecting DDoS attacks was proposed and created for SDN controllers. The ML approach mainly employs three classification algorithms: Extreme Gradient Boosting (XGBoost), Random Forest (RF) and Decision Tree (DT), and it is evaluated using the CICDDoS2019 dataset. The results of the research demonstrate the effectiveness of ML techniques, achieving remarkable accuracy scores of 99.94%, 99.93%, and 99.87%, respectively, for the aforementioned algorithms. These results highlight the ability of ML algorithms to accurately detect DDoS attack types in SDN environments.
Mass dependence of energy loss in collisions of icy spheres: An experimental study

By varying the mass of icy spheres which collide with large ice blocks, we have determined the mass dependence of energy loss in such collisions. We find, at commercial deep freeze temperatures, that collisions at 1 cm/s become quite inelastic for small iceballs and that the rate at which elasticity decreases with decreasing mass roughly coincide with that predicted by a viscous dissipation model. If such a mass dependence persists at much lower temperatures, it could lead to very large effects in planetary ring systems (rings and dust, ice, surfaces, origin, and evolution).