Miracle-dz/UniFAR-FTUs huggingface dataset:
Mango Farming Optimization With AI: Boosting Cultivation Efficiency

Mangoes have long played an important role in global import and export, but a fall in mango commerce has been seen over the past few years, preventing producers from meeting market demand. The predominant cause of this loss is the prevalence of mango tree disease, which finally transforms healthy and fruitful mango orchids into low-yield varieties. Our proposed approach offers a "Mango Leaf Health Detection Dataset" for mango tree health detection. The dataset has been validated and tested with YOLO v5s, YOLO v7, and YOLO v8s. The YOLO Algorithm analyzes the image of the leaf by passing it through a pipeline consisting of redefining the image in grid cells, constructing bounding boxes, and predicting the class. This not only saves time, but also delivers accurate and effective results, thereby designating the area of the orchid where pesticide must be treated. Our trained YOLO v7 detection video can be found ${\color{Blue}\text{here}}$, and the dataset used can be accessed ${\color{Blue}\text{here}}$.
A generic-based Federated Learning model for smart grid and renewable energy

The liberalization of the electricity market and the expansion of new forms of electricity production and consumption are paving the way for new smart digital services. These new services will certainly be relying on a new generation of smart meter (SM) that will offer, among other things, prediction of consumption and production at both household and microgrid levels.These predictions can be obtained either through a generic model trained on data collected from all SMs, or through specific models developed for each SM based on its individual data. The benefit of the generic model is that it guarantees an optimal solution. However, its implementation is not possible for security reasons. The use of specific models requires managing a large number of SMs, which poses a significant challenge.This paper presents a Federated Learning (FL) approach, a decentralized privacy-preserving paradigm that achieves com-parable performance to the generic model, considering both consumption and production scenarios.The dataset, gathered from 1153 SMs over a period of 18 months, is provided by a Swiss Distribution System Operators (DSO). Although, data are individually collected per device, the generic model is trained to holistically process this data.Our experimental results demonstrate that our FL based Long Short-Term Memory (LSTM) model performs as well as the generic model and outperforms the specific models, while preserving data privacy and security.
Disease Identification in Potato Leaves Using a Multi-Tier Deep Learning Model

To prevent production loss, planning for and responding quickly to disease attacks on vegetable plants, such as potatoes, is essential. The leaves and stems of vegetable plants are typically the first to show signs of disease. Thanks to recent developments in deep learning algorithms, it is now possible to use leaf images for disease classification. The primary goal is to develop a deep learning-based system for forecasting and classifying crop leaf diseases. In this study, potato vegetable crops are examined. Both the training and testing data came from a publicly available source. The system was built and evaluated using Inception V3 of the convolutional neural network and the SGD, SVM, and Logistic regression classification algorithms. Therefore, the system's performance is expected to reach a new peak.
Simulation of Resonances in Power Electronic Circuits for EMC Prediction

The use of wide-bandgap semiconductors in modern power electronics creates new challenges in EMC. These result, among other things, from high frequencies and steep switching slopes. In addition, parasitic elements of semiconductors, passive components and the printed circuit board can cause resonances, which become noticeable in oscillations. In this work, a possibility is presented to simulate resonances in a power electronic circuit. The circuit used is a series-LC resonant converter with SiC-MOSFETs. An impedance model of the resonant load is generated with consideration of parasitic elements. The accurate simulation is validated by comparison with time domain measurements. EMC measurements of conducted and radiated emissions are done. It is shown that the resonances lead to emissions which can be predicted using the time domain simulation.
Stratospheric water vapor content evolution during EASOE

During EASOE, 7 in situ stratospheric water vapor content measurements were performed, in the polar region, with a balloon-borne frost-point hygrometer, from November 1991 until March 1992. The comparison between the frost-point and the air temperature profiles shows on 11 December the occurrence of PSC formation conditions when a PSC was observed by lidars. Outside the vortex the mean water vapor content seems to be very stable, 4 to 5 ppmv from 16 to 25 km. But is very changeable below 16 km, due to the variability in the origin of the air masses where the measurements occur, as these air parcels come sometimes from the tropics, then bringing dry air. Inside the vortex, a vertical descent of the air masses is observed, and is estimated to be 4 km around 25 km at the end of March, before the vortex breakdown. The rehydration of the vortex induced by this descent, from February to March, is shown.
A Comprehensive Survey on Fake News Detection with Machine Learning Algorithms

Using AI to fight fake news efficiently. Using AI to fight misinformation with more modern approaches from machine learning and natural language processing. This paper discusses on extraction, model selection, training, assement, deploying, and continuous learning. The process includes extracting of tags from all over social media and headlines and quantifying text with AI. The models are chosen using machine learning methodologies such as Naïve Bayes, Support vector, and neural networks. Differentiating fake news and training for satire/Irony and adapting to newer kind of fake trends (image, videos) will help in implementing this effectively.
An Investigation of the Security and Privacy Implications of fog Computing Systems using IFogSim

Fog computing, a concept that extends cloud computing capabilities to the edge of the network and enables effective data processing and analysis, has drawn a lot of interest. However, in order for fog computing systems to be successfully deployed, significant issues of security and privacy must be resolved. This research study uses IFogSim, a simulation tool made exclusively for fog computing, to examine the security and privacy issues of fog computing systems. In order to uncover possible security and privacy threats and assess the efficacy of various security solutions, we model and simulate various fog computing scenarios using the capabilities of IFogSim. Understanding the effects of assaults like malware insertion and data exfiltration on the functionality and security of fog computing systems is the main goal of our research. Additionally, we investigate various security methods, such as access control guidelines, authentication procedures, and encryption, to evaluate how well they may reduce the threats that have been discovered. The research's findings shed important light on the privacy and security issues that fog computing systems raise. They assist in determining the proper security measures to protect sensitive data and inform the design and development of secure fog computing systems. The trade-offs between security and performance are also highlighted in our study, allowing decision- makers to find a compromise between these important factors.
Influence of nonlinear circuit components on the creation of intermodulation

To enhance the research of intermodulation distortions (IMD) in the field of EMC a nonlinear circuit was built to simplify the measurement of IMD. A simulation was carried out in order to study the influence of various nonlinear components on the creation of IMD. The first part of this work summarizes the general and most important influences of the static and dynamic characteristic curves on the creation of IMD. The second part presents some measurement results in order to verify the simulated results. These measurements were made with a developed circuit containing either a Schottky diode or a voltage comparator and a excitation with frequencies of 19 kHz and 21 kHz.
Development of a Virtual Balloon Angioplasty Treatment Simulation Device Based on the Spring-Damper Method

Stroke is one of the chronic conditions that lead to an 85% of death rate from cardiovascular disease in this world. A stroke is when brain cells will not work correctly or even stop working because brain cells do not get enough oxygen from the bloodstream. Most stroke cases are caused by plaque accumulation inside the blood vessel walls. This kind of condition is commonly known as stenosis. One of the methods for treating stenosis is using balloon angioplasty (BA) to revascularize the blockage area of plaque layers inside the vessels. Most ICAS rehabilitation devices are expensive because were imported from other countries. In order to reinforce the BA fabrication locally, we develop an in silico simulation program to represent the actual phenomenon physically during the stenosis treatment using BA. Thus, the result given will be a reliable recommendation for device fabrication.The BA deployment algorithm is virtually implemented using a spring-damper model analogy with the lineal spring approach. This approach allows obtaining physical information such as pressure along the blood vessel model. Thus, it can be evaluated regarding the standard clinical reference about blood vessel conditions. The simulation results were given in the meantime for 5 - 106 seconds, depending on the several parameters used. The pressure distribution of revascularized blood vessels was also obtained, assuming a uniform modulus of elasticity.
Surface Subsidence Monitoring in Jinan City Based on SBAS-InSAR Technology

In this paper, we used the Sentinel-1 images of the 37 ascending orbit and 37 descending orbit covering Jinan City from April 2018 to April 2021 to acquire the vertical surface subsidence data of Jinan City based on SBAS-InSAR technology, conduct surface subsidence monitoring in the urban and suburban areas of Jinan City, and validate the accuracy of the ascending and descending orbits results. The accuracy validation showed that the Sentinel-1 ascending and descending orbits monitoring results were highly consistent. It was found that the subsidence areas in Jinan are mainly located in the industrial parks in the western part of Tianqiao District and the northern part of Zhangqiu District, as well as in the area of Jinjia Village - Gumiao Village - Yaojia Village in Tianqiao District. The maximum average annual rate of subsidence in the industrial park in Zhangqiu District can reach -101.97mm/a, with a cumulative subsidence of over 250mm, and a subsidence funnel has been formed. The research shows that the subsidence in both industrial parks is related to the exploitation of groundwater from the parks' own wells, and that the subsidence in the area of Jinjia Village - Gumiao Village - Yaojia Village is closely related to coal mining in the area.
Characterization Setup of a Molecular Iodine Based Optical Clock : Long-term Optical Frequency Generation for Time and Frequency Transfer Experiments

SummaryIn this mansucript we present a setup and first results for long-term operation of an optical clock based on an existing Doppler-free iodine vapor cell spectroscopy unit [1] acting as an optical frequency reference system and an optical frequency comb. The combination of comb and optical frequency reference represents an ultra-stable radiofrequency source, as the comb is transferring the stability of the optical frequency reference to the radiofrequency regime. The radiofrequency is therefore generated directly from the pulse train of the optical frequency comb. The frequency comb is optically phase locked to the optical frequency reference based on molecular iodine, resulting in a full optical-to-radiofrequency chain. We will present the phase and amplitude noise performance of this radiofrequency source, i.e. an optical clock. The short-term stability in terms of Allan deviation is determined in an optical comparison measurement with a cavity stabilized laser system with a sub-Hz linewidth via the frequency comb. However, our main focus lies on the investigation of the long-term stability of the clock chain in the radiofrequency domain in comparison to the UTC realization of the German Aerospace Center (represented by the Galileo Competence Center) and the continuous operation of an optical clock.
Frequency Based Gait Gender Identification

This paper addresses the problem of identifying the gender of people based on their walking patterns. In the proposed system, people are initially detected, and silhouettes are extracted from each frame after background subtraction. This information is used to analysis in the frequency and spatial domains of Gait Energy Images. In this study, four subjects with different gaits are analyzed walking normally, carrying a backpack, carrying a suitcase, and wearing a coat. Three alternate classification methods are investigated in experiments with spatial and frequency domains of different subjects. The best gender identification performance was obtained with frequency domain based on the Adaboost classifier. The experimental results of the gender classification are 97% for the Normal vs. Normal dataset, 90% for the Normal vs. Case dataset, 83% for the Normal vs. Coat and 84% for the Normal vs. Backpack dataset.
COPDNet: An Explainable ResNet50 Model for the Diagnosis of COPD from CXR Images

Chronic Obstructive Pulmonary Disease (COPD) is a prevalent pulmonary condition marked by enduring respiratory symptoms and airflow limitations. Prompt diagnosis is vital for efficacious disease management and enhanced patient outcomes. This study introduces COPDNet, an explainable deep learning model founded on the ResNet50 architecture, tailored specifically for diagnosing COPD using Chest radiographic (CXR) images. COPDNet employs preprocessing techniques such as normalization, resizing, and data augmentation to boost model performance and generalization. To foster interpretability and clinician trust, explainability methods like Layer-wise Relevance Propagation (LRP) or Grad-CAM are integrated, offering visual explanations for model predictions. In addition, transfer learning is utilized by initializing the model with pre-trained weights from extensive datasets, minimizing training time and refining performance. With a recall of 98.9% on the pneumothorax CXR image, the efficacy of COPDNet in diagnosing COPD was validated, potentially leading to improved patient outcomes and supporting clinicians in making well-informed decisions.
Impact of geolocations of validation data on the evaluation of surface incident shortwave radiation from Earth System Models

Ground‐based observations of surface incident solar radiation (Rs) have been used to evaluate simulations of global climate models. Existing studies have shown that biases in simulated clouds have a significant spatial pattern, which may be transferred to the simulated Rs. Therefore, the evaluation results of Rs simulations may depend on the locations of the ground‐based observations. In this study, Rs simulations of 48 models participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) were first evaluated with ground‐based observations from different networks (446 stations in total) from 2000 to 2005. The global mean biases of the CMIP5 Rs simulations were found to vary from 4.8 to 11.9 W m−2 when Rs observations from different networks were used as reference data. To reduce the location impact on the evaluation results, CMIP5 simulated Rs was then evaluated with the latest satellite Rs retrieval at 1° × 1° spatial resolution by the Clouds and the Earth's Radiant Energy System, Energy Balanced and Filled (CERES EBAF). It was found that the CMIP5 simulated multimodel mean Rs has a small bias of 2.6 W m−2 compared with CERES EBAF over the globe, 4.7 W m−2 and 1.7 W m−2 over land and oceans, respectively. CERES EBAF Rs was found to have a positive bias of 1.3 W m−2 compared with ground‐based observations. After removing this bias of CERES EBAF Rs, global mean Rs was estimated to be 185 W m−2.
Sentiment Analysis of Social Media Reviews using Machine Learning and Word Embedding Techniques

The enormous amount of data generated and exchanged daily on social media raises the need for knowledge-extraction tasks to be mechanized. Sentiment analysis is an active area of information extraction research with various challenges to overcome. Any business ought to be highly interested in client feedback. Customers mainly rely on reviews to determine where to dine. Sentiment analysis is vital in classifying restaurant reviews into positive, negative, and neutral polarity to determine whether the cuisine is good, securely produced, and worth picking over other restaurants. The Yelp dataset for restaurant reviews is used in this study to test different word embedding approaches, including Bag of Words (BoW), Term Frequency-Inverse Document Frequency(TF-IDF), GloVe, Word2Vec, and Doc2Vec. Supervised Machine Learning (ML) algorithms like Logistic Regression (LR) and Support Vector Machine (SVM) are evaluated based on the performance metrics such as the F1-Score, Accuracy, Recall, and Precision. Comparative findings demonstrate that combining a Support Vector Machine and TF-IDF word embedding technique produced results with an accuracy of 98%.
RJB-Net: Residual Deep Learning with Joint Bilateral Denoising Network for Remote Sensing Image Fusion

Remote sensing image fusion amalgamate information from panchromatic and multispectral remote sensing images to generate an optimum representative image. In this paper, we have proposed a novel method for image fusion known as Residual Deep Learning with Joint Bilateral Denoising Network (RJB-Net). The RJB-Net procedure is initiated with panchromatic image denoising using a joint bilateral filter to suppress noise and retain edge information. RJB-Net model is trained at image patches covering all major spectral classes. The denoised panchromatic image along with multispectral data is input to the residual deep learning network for inference and generates a fused image. The remote sensing datasets used for RJB-Net training and evaluation are Indian Cartosat-1 panchromatic and Resourcesat-2 multispectral imagery. The experiment section compares the RJB-Net denoising output with prominent spatial domain image filtering methods both qualitatively and quantitatively. It is found that RJB-Net fused images inject the spatial information from panchromatic images in a controlled manner while retaining the spectra characteristics of multispectral data. The merging performance of RJB-Net is compared with state-of-the-art remote sensing image fusion techniques that range from classical to recent deep learning model. The proposed method RJB-Net generates superior results among all fusion methods at different feature targets and compared the performance both visually as well as with image quality metrics.
Achieving Medical Image Classification of Alzheimer’s Disease by the Implementation of Different Image Enhancement Methods Based on VGG

Aimed to solve the classification problem in related to diagnosing the Alzheimer’s Disease, thousands of relevant images which had processed by various enhancement methods were fed to neural network to train a useful model. The class of our images includes NonDemented, VeryMildDemented, MildDemented and ModerateDemented. We used CNN, a well-known machine learning model and furthermore, its standard structure VGG was also applied by us to increase the accuracy of it. In the end, the training accuracy of our model has reached 100%, and the test accuracy is over 99%. It is fully convinced that our model can make some contributions to medical diagnose.
An Overview of Advanced Gate Driver Concepts for SiC Semiconductors

Silicon carbide (SiC) power transistors have been steadily gaining popularity in power electronics applications as they have improved properties over silicon based devices, namely silicon MOSFETs and IGBTs. However, because of very high switching speed which results in lower losses, in general application their use is often a concern because of electromagnetic interference (EMI). In order to fully utilise the potential of SiC based devices, a careful design of power stage, commutation loop and gate drive is needed. Because of this reason, this paper presents a review of existing gate drive methods for performance improvement of SiC devices. A special emphasis is given to the gate drive concepts, as it is an important element for the further advancement of SiC-based converters. With improved switching, specifically active gate driving techniques a compromise between EMI and switching losses can be achieved.
Matrix Model of Continuous Rolling Mill as Automatic Control Object

As digital technologies develop and digital twins are being actively adopted, the current rolling production development stage requires elaborating the universal models of interconnected electromechanical rolling mills systems. The introduced virtual models may have different structures and various level of detail. A promising area is matrix model building; however, the disadvantages of such models include the complexity of specific physical parameters analysis and controller synthesizing. Eliminating the existing shortcomings is possible by developing a dynamic mathematical model of a continuous n-stand mill as a multi-connected system, including a mathematical description of the unwinder and winder. The model is characterized by a high clarity, its structure in a matrix form is similar to the structure of a single stand which significantly simplifies the analysis of physical processes. The model was used to develop an automatic system controlling the geometric dimensions of a continuous wire rolling mill with multi-roll calibers. The material provided is a part of a complex research work aimed at generating aggregated digital counterparts of rolling production units
Microelectronic Vibration-Frequency Pressure Measuring Converter

Measuring transducers of non-electrical quantities currently have the widest application in engineering and technology, in particular, in aviation, in space technology. Vibration-frequency converters of mechanical quantities, in particular, pressure and temperature, are widely used in this direction. The positive properties of vibration-frequency converters consist in a number of their unique properties: the principle of converting these converters consists in a conversion method based on the frequency method, in which the frequency is an informative parameter; this is a very efficient, in terms of energy consumption, conversion principle. There is no need to use frequency to digit conversion. At the same time, it is possible to achieve high measurement accuracy, since when using widely available frequency meters, it is possible to measure fractions of hertz, providing accuracy in fractions of a percent. Currently, semiconductor structures are used as sensing elements, which have small dimensions and high sensitivity, which makes it possible to drastically reduce the dimensions of the converters and use them in space and aviation devices.
Optimizing Hollow Core Fibers for Stable Interferometry

SummaryWe present a hollow core fiber (HCF) with optimized thickness of coating and silica glass, calculate its thermal sensitivity including the effect of coating and compare it with earlier-reported HCFs. We experimentally demonstrate the optimized HCF with thermal phase sensitivity as low as 1.64 rad/K/m, which is 30 times below that of the standard single mode fiber.