Miracle-dz/UniFAR-FTUs huggingface dataset:
Crop Recommender System Based on Ensemble Classifiers

Farmers are facing problems because they are unable to manage cultivation because of bad weather conditions and uneven rainfall. Thus, to reduce the problems of farmers, the latest technologies are introduced such as machine learning to implement crop recommendation systems. A wide range of classification techniques are used, and a specific model is selected based on their accuracy levels. By using feature selection techniques, the raw data is converted into a dataset which is useful for efficiently training the model with relevant data. Reducing redundant data and utilizing just the aspects that are significantly relevant in deciding the model’s final output will improve the model’s accuracy. The findings show that, compared to other classifiers, the ensemble approach delivers better prediction with a 99.54% accuracy rate. document is a ‘‘live’’ template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.
Pre- Stroke Detection using K- Nearest Neighbour and Random Forest Algorithm

Stroke is one of the deadliest diseases in the world because it causes the brain's blood vessels to burst, injuring the brain. Symptoms may appear when the brain's blood and other nutrient flow is interrupted. There are various imaging techniques to detect stroke like CT, MRI etc., but these techniques are expensive, time consuming and in these techniques, people need to depend on radiologists for disease diagnosis. The existing model incorporates only software prediction so real time prediction is not possible and also early detection of stroke cannot be predicted so that the treatment given for stroke gets delayed and the severity of the disease is increased To overcome this the proposed system uses a microcontroller and various types of sensors to detect the vital parameters like heart rate, SpO2, temperature, lump, and it also uses machine learning algorithm to detect the stroke in advance. For the accurate detection of the stroke, an efficient Machine Learning technique should be used, and it was created through a unique examination of many ML algorithms. KNN and the random forest algorithm were two machine learning algorithms employed in this case to recognize strokes. The accuracy level of KNN is less than random forest algorithm that is 52% and 94% respectively.
Exploring The Potential of VR Interfaces in Animation: A Comprehensive Review

Creating computer animation takes a lot of work and time, it is true to say that animation takes a labor-intensive business. Virtual reality (VR) is then playing quite attractive role in the field of animation where they are looking for an innate and methodical option to today’s rich user interfaces. Earlier work in the field of the animation shows maximum interaction potential of VR-based interfaces. This also expresses the increased spatial awareness resulting from the high immersion flexibility it provides in virtual environments. Existing virtual reality interfaces offer heterogeneous subsets of traditional tools and their functionalities. VR interfaces provide the atmosphere of a real environment to animators. These interfaces make animators feel the environment they are trying to create. Researchers are trying to present more functionalities on the existing animation tools to make them less complex and time taking. This review describes briefly the animation tools, VR-based interfaces, and related work on VR interfaces and areas explored on VR-based interfaces for 3-D animation.
Hybrid Image Classification Model using ResNet101 and VGG16

Deep convolution neural networks have made sig-nificant advances in object identification. The popularity of machine learning-based image classification has increased as a result of developments in deep learning algorithms that makes it possible to extract features from images. Yet, conventional image classification algorithms are far too incorrect and untrustworthy to address the problem. Automation is crucial due to the vast geographic areas that must be explored and the scarcity of researchers available to carry out the searches. The proposed work employs deep learning-based image classification using a hybrid model of ResNet101 and VGG16 to address the challenges of image classification in large geographic areas using satellite images.
Unscented Kalman Filter Based Protection Level for the Integrity of Space Launch Vehicle

A horizontal protection level (HPL) calculation framework for the integrity monitoring of a highly non-linear dynamic system (space launch vehicle) is presented. The protection level is calculated after outlier detection is performed in the faulty measurement sets. The Unscented Kalman Filter (UKF) concept is adopted for the state estimation and protection level calculation for the launch vehicle. The minimum detectable bias obtained from the outlier detection strategy and the innovation covariance from the UKF are used to calculate the protection level which satisfies the conditions of nominal operations. This framework is tested using simulated global navigation satellite system (GNSS) pseudorange measurements and the second order differential equations of the launch vehicle dynamics.
Multipath Detection and Mitigation from GNSS Observations Using Antenna Arrays

We consider the problem of detecting and mitigating the effect of multipath on GNSS observations. In particular, we focus on carrier-phase observations that are collected at an array of GNSS antennas. We exploit a special antenna array geometry, synchronization among the GNSS receivers, and an attitude estimate provided by an attitude filter or an IMU, to develop a technique to identify the satellite observations that are contaminated with multipath. The proposed technique leverages the antenna geometry to rabidly estimate the attitude parameters for various satellite combinations. Next, a dedicated decision-making algorithm is used to identify the satellite observations affected by multipath. Our simulation results demonstrate the effectiveness of the proposed approach in detecting the occurrence of multipath with high success rates. By rejecting the multipath-affected observations, we show remarkable performance gains when attitude determination is considered as an example application.
Capabilities of a Real-Time EKF Vector Tracking Algorithm for Dynamic Flight Maneuvers With Small UAVs

To strengthen tracking in dynamic flight maneuvers the miniaturized real-time Global Navigation Satellite System (GNSS) receiver GALileo ANTenna (GALANT), developed by the German Aerospace Center (DLR), has been equipped with a robust vector tracking algorithm. To harden the receiver the algorithm used on the platform excludes Non-Line-Of-Sight signals by exploiting spatial information gained by the 2×2 antenna array and its processing. This contribution focuses on the planning, execution and evaluation of the experimental validation of the receiver and its tracking algorithm by conduction of open field flight test in the rural area near Munich. The developed system was fitted as payload to a compact hexacopter and the potential benefits in signal tracking are investigated, while the algorithm relies on GNSS information only and uses no other sensor data input such as Inertial Measurement Unit (IMU) or compass. The receiver has been developed to be resilient to intentional and unintentional interference from diverse origins based on spatial suppression of jamming sources, which has been proven in previous tests. Performing these techniques also suppresses satellite signals impinging from the direction of the interference source, which need to be recovered. To test the performance of the implemented vector tracking algorithm with respect to the signal recovery, the receiver is exposed to a jamming source on ground while changing the constellation.
BCD-Unet:A Novel Water Areas Segmentation Structure for Remote Sensing Image

Surface water resource identification is one of the main techniques used in remote sensing image analysis. This is necessary to stop calamities like floods and droughts. Feature selection based on prior information and complex spectrum analysis are required by machine learning algorithms for extracting waterbodies. The use of deep learning-based algorithms, which have just lately been considered, has eliminated the need for manually extracting features, but they require a large amount of training data and processing power to achieve outstanding performance. Because of this, each deep learning architecture that has been demonstrated may recognize some of the patterns that are present in the given situations. To develop a reliable model for identifying surface water, this research trains and optimises deep learning architecture that have been presented in different disciplines using surface water data. This is accomplished by the model’s output more accurately by extracting the finalized segmented mask of water bodies. Our suggested approach was tested using a Kaggle dataset. This dataset included Sentine1-2 satellite images of numerous water bodies. The suggested method outperforms current best practises in semantic segmentation performance.
Global Air Pollution Data for Health Risk Assessments in Lombardy, Italy

Air pollution figures among the top 10 of the Disability Adjusted Life Years (DALY) risk factors. Poor air quality particularly impacts the urban population. In 2021, in Europe, 91% of it was exposed to pollutants’ concentrations above the 2021 World Health Organization (WHO) air quality guidelines (WHO, 2021a). For this reason, performing a health risk assessment due to the exposure to air pollution is important to support policy makers and to guide the implementation of mitigation measures. Several data sources of surface air pollution concentration are available today, especially for high-income countries. However, each of them brings strengths and weaknesses in terms of spatiotemporal resolution and coverage. Moreover, data are lacking in many regions of the world. With this study, we present an evaluation of the performance of the European Centre for Medium-Range Weather Forecast (ECMWF) Copernicus Atmosphere Monitoring Service (CAMS) global reanalysis dataset (EAC4), with coarse resolution and worldwide availability, with those of the ECMWF-CAMS European Reanalysis, with higher resolution. This is done in sight of an application of these data to perform a health risk assessment due to a long-term exposure to NO2, PM2.5 and O3. Data aggregates are put in relation to the Fraction of Settled Area (FSA) of the World Settlement Footprint and diurnal variability of the species’ concentrations. The geographical area of this study is the highly urbanized Lombardy region in northern Italy. However, the results are discussed with respect to global applicability.
Virtual Cable: Beam-Direction Converter for IoT Communications inside a Motor

In the automotive and robotics industries, there is an increasing demand for sensing conditions inside metal housings such as engines and motors. The inside of metal housings is narrow and complex, and wiring from the outside of the metal housing to the sensor is difficult due to safety considerations and limitations on the range of movement. To overcome these limitations, wireless communication to transmit sensor data outside of a metal enclosure is a possible solution. However, this solution suffers from reflection and multipath inside the metal enclosure, resulting in a large propagation loss of radio waves. In this study, we reduced the propagation loss by developing a direction changer that combines a small spiral antenna and a small directional antenna made of a printed circuit board to limit the direction and radiation angle of radio waves, thereby suppressing the reflection and scattering of radio waves. Experiments using a test device imitating a motor confirmed that the proposed method improved the received signal strength indicator by approximately 30 dB compared to that obtained without the direction converter.
Accuracy Assessment of Two GPS Fidelity Prediction Services in Urban Terrain

Low altitude flight in urban areas is susceptible to degraded GNSS-based navigation system performance due to terrain interference with radio signals from orbital positioning satellites. Predictive navigation performance fidelity tools are needed a) in preflight planning to assist in the creation of safe flight paths and b) in-flight to provide contingency management agents with the navigation risk of proximal flight corridors. Two navigation fidelity prediction services are validated by comparison with over 6000 readings from GNSS sensors collected along a five-mile path through urban areas of Corpus Christi, Texas, on three dates in 2022. Predictions are based on satellite line of sight through 3D terrain data collected in 2018. Each service predicts a set of navigation fidelity metrics over a user-specified time period. One metric estimated by both is the number of visible satellites. A direct comparison of the number of predicted visible satellites with the number sensed by the receiver is used to validate the prediction services. Results show an exact match in the number of predicted satellites for 60% of the measurements, and a match within +/- 4 satellites for 95% of the measurements. As expected, agreement improves away from vertical blocking terrain. Most cases of mismatch are due a lower predicted count than measured (false negatives), and can be accounted for by receiver pickup of stray signals caused by multipath propagation. About 10% of mismatches are false positives and are mostly accounted for by foliage effects. The two services predict visibility of the same set of satellites 80% of the time, differ by two or less satellites 95% of the time, and can compute predictions for one hour of observations in one minute or less. Validation is analyzed statistically and in detailed case studies of selected observation times. The prediction services validated in this study run fast enough for preflight safety planning. The more stringent challenge of in-flight navigation fidelity prediction for contingency management requires both a speedup of the current level of modeling and equally fast stray signal modeling.
Image Denoising Based on K-means Singular Value Decomposition

The image is usually polluted by noises in its acquisition and transmission and noises are of great importance in the image quality, therefore, image de-noising has become a significant technique in image analysis and processing. In the image de-noising based on sparse representation, one of the hot spots in recent years, the useful image information has certain structural features, which coincide with the atomic structure while noises don’t have such features, therefore, sparse representation can separate the useful information from the noises effectively so as to achieve the purpose of de-noising. In view of the above-mentioned theoretical basis, this paper proposes an image de-noising algorithm of sparse representation based on K-means Singular Value Decomposition (K-SVD). This method can integrate the construction and optimization of over-complete dictionary, train the atom dictionary with the image samples to be decomposed and effectively build the atom dictionary that reflects various image features to enhance the de-noising performance of the algorithm in this paper. Through simulation analysis, this method can conduct noise filtration on the image with different noise densities and its de-noising effect is also better than other methods.
TransEN U-Net: Enhance Image Segmentation of Cytomegalovirus Infected Cells in Histopathological Images

Advances in histopathological image segmentation have a significant role in the diagnosis and begin treatment immediately including a study of Cytomegalovirus(CMV) for the tissues. Histopathological change with confirmation by immuno-histochemical or in situ hybridization study is a gold standard for diagnosis of CMV tissue infection. This required pathologists to identify the histopathological change which is time-consuming and can be missed in tissue with a subtle change. Automatic analysis of histopathological images with Deep Learning(DL) can help pathologists to diagnose CMV tissue infection more accurately. Typical issues with histopathological images which impede automatic analysis are the extremely enormous size of histopathological images more than 1 gigapixel, the limitations of GPU memory, and a limited number of histopathology images. Additionally, whole slide histopathological images are split huge images into multiple small image patches by cropping using the sliding window technique. In this paper, we propose TransEN U-Net which derives a benefit of a hybrid CNN-Transformer base on the U-shaped architecture for boosting the performance of segmentation of histopathology. The transformer encoder not only is able to the patches but also the relative self-attention mechanism in order to share information between sequences. Experiment results of segmenting images by the two-dimensional indicate that the TransEN U-Net can productively discriminate CMV viral inclusions including achieving higher values in terms of DSC score.
Component Identification and Defect Detection of Printed Circuit Board using Artificial Intelligence

This Printed circuit board(PCBs) flaws are identified and detected using artificial intelligence. The necessity for effective and precise inspection procedures in the production of electronic devices is a result of the rising demand for high-quality electronic products. The suggested technique makes use of a deep learning model that was trained on digital microscope pictures of PCBs. The AI model can reliably recognize different components on the PCB and find any flaws, including broken trace lines, missing components, and improper component placement. With an average precision of 99.6% for component identification and an average precision of 98.7% for defect detection, the results demonstrate that the AI model performs with a high degree of accuracy. The effectiveness and dependability of PCB inspection and quality control processes can be greatly increased by putting this strategy into practice
Relative Navigation Methods for a Multi-Agent, On-Orbit Inspection Mission

On-orbit inspection is often a necessary prerequisite to satellite operations such as servicing and debris removal. In particular, multiple inspectors collaborating to inspect an unknown object can result in a faster and more comprehensive inspection. The contribution of this research is to provide a reliable, computationally efficient estimate for the position of each agent relative to a target in a multi-agent inspection. First, an overview of the problem dynamics, background, and methods used to build and solve a factor graph-based estimation technique is provided. Through the implementation of a Sliding Window (SW) filter, a factor graph can be applied to long duration scenarios while reducing processing requirements for estimation. An example scenario of three agents attempting to conduct an inspection while susceptible to a Sun exclusion zone that nulls some measurement information is prescribed. Development continues by comparing results for the developed Sliding Window Factor Graph (SWFG) against two extreme SW sizes of interest: an iterative Kalman filter (SW size = 1) and a full factor graph (SW size ≥ scenario duration). Results reveal the impact of both the Sun exclusion zone and sliding window size on estimation quality and computation time.
YarowskyDroid: Semi-supervised based Android malware detection using federation learning

In this paper, a novel approach is proposed entitled as “YarowskyDroid”, that works on the principle of semisupervised machine learning approach and federation learning to detect malware-infected apps. In order to protect user privacy, apps are exclusively installed locally on the user’s smartphone. This prevents service providers or developers from learning which apps a user has downloaded. Meanwhile, information from smartphone users is gathered in order to improve the malware detection algorithm. The primary issue in this study is that users cannot tell whether an app they have loaded is contaminated with malware or not. In order to overcome this problem, a semi-supervised machine learning technique is proposed in this study that improves classification accuracy on comparison to the base model set up in the cloud. A experiment was carried out using 50,000 malware-free and 25,000 malicious app downloads from different repositories. The empirical finding shows that the suggested framework, with 210 users and 40 rounds of the federation, has a detection rate of 97.9%.
UAV-Aided Indoor Localization of Emergency Response Personnel

Unmanned aerial vehicle (UAV) aided communication systems have been gaining attention due to their wide applications in public service, military sectors, and emergency services as well as their flexibility in deployment. In this paper, we propose using 5G positioning techniques and collaborative localization to accurately estimate the locations of first responders located inside the building using outdoor UAV-mounted base stations (BSs) serving as anchors. For this setup, we perform a comprehensive Cramér-Ran lower bound (CRLB) based analysis to evaluate the performance of position estimation in 3D and to examine the sensitivity of the localization performance to different system parameters. Our analysis considers various system parameters: dynamic line-of-sight (LOS)/non-line-of-sight (NLOS) characteristics of the channel, geometry of the anchors, number of anchors, number of LOS anchors, NLOS bias between an anchor and a target node, and between target nodes, anchor location uncertainty, and synchronization errors among anchors. Our analysis uses time-difference-of-arrival (TDOA), and time-of-arrival (TOA) based positioning methods assuming both LOS and NLOS propagation. Through our analysis, we identify the system parameters that most significantly impact the localization performance as well as the scenarios that provide location accuracy or position error bound (PEB) on the order of 2m and 3m.
Relationship between Precipitation Rates at the Ground and Aloft—A Modeling Study

Abstract Two cloud-resolving mesoscale models, the Karlsruhe Atmospheric Mesoscale Model (KAMM) and the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), were used to study reflectivity factor–rain-rate (Z–R) relationships and instantaneous and horizontally averaged profiles of precipitation rate for convective storms of varying intensity. Simulations were conducted for idealized terrain. KAMM modeled a single shower cloud; MM5 was used to study split-storm supercell development. Both models consistently confirm analytical results from earlier studies: convective drafts and stratification of air density significantly alter the local rain rate and, therefore, also any Z–R relation relying on conditions of stagnant air and sea level air density. Air density effects can be almost completely corrected for by a recently proposed algorithm, but effects of convective drafts remain. They can lead to upward precipitation mass fluxes of significant magnit.
A multi-objective deep learning based approach for SAR image reconstruction in urban environment

SAR sensors have become a crucial instrument for Earth observation. Interferometric SAR (InSAR) systems provide complex images whose phases contain information on the height profile of the scene, but at the same time are affected by noise. Therefore, InSAR phase denoising is a fundamental pre-processing step for easing and improving the performance of further applications. Urban areas are very challenging to be characterized and usually require a specific filtering design. In this work the result in urban areas of a CNN solution trained on natural scenarios with a realistic dataset, are exploited. The results show a good generalization ability thanks to the wide and realistic dataset and to the multi-objective nature of the designed cost function.