Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of a compact antenna and rectifier for a dual band rectenna operating at 2.4 GHz and 5.8 GHz

In this article, a multiband compact patch antenna and a multiband rectifier circuit is proposed for a rectenna operating at 2.4 GHz and 5.8 GHz for RF energy harvesting and wireless power transmission. A modified inverted F shape antenna is used as the main receiving component of the rectenna. The antenna has a compact size of 35×40 mm2. The proposed antenna has good impedance bandwidth (for S11< -10 dB) values of 230 MHz and 620 MHz at the resonant frequencies 2.4 GHz and 5.8 GHz, respectively. The gain values are 3.6 dB and 3.3 dB at 2.4 GHz and 5.8 GHz, respectively. Furthermore, an efficient dual band rectifier circuit is presented. The rectifier circuit is a single branch and consists of a matching network, voltage doubler circuit and a load. The proposed rectifier circuit is designed on RT-Duroid 5880 substrate and the HSMS2850 diode is used for rectification. The rectifier achieves very good efficiency at both frequencies i.e., 71 % and 51.9 % at 2.4 GHz and 5.8 GHz respectively.
Modal Analysis and Sound Field Modeling of a Permanent Magnet Axial Flux Machine with One Stator and Two Rotors

There is a potential for increasing torque density, as well as energy efficiency of modern electric vehicle systems by incorporating axial flux machines for certain applications where these characteristics are of interest. High torque density electric machines have inherently worse acoustic behavior, while axial flux machines are still not thoroughly investigated in this area, or produced for mass applications. Single stator double rotor yokeless AFM has a high potential for automotive applications, and has to be investigated in this context, while fulfilling ever-increasing acoustic requirements. The paper presents analytical and model-based considerations of acoustic behavior of an axial flux machine, completed by combining force excitation results obtained by finite element analysis and sound field output from analytical vibratory and acoustic models.
Deep Learning based Driver Drowsiness Detection

Sleepy and distracted drivers are increasingly being seen as major causes of fatal car accidents across the globe. This happens most often because the driver hasn't had enough sleep, but it may also be the consequence of untreated sleep disorders, substance abuse, or working odd hours. That's why it's crucial for cars to have systems in place to track and verify the identities of their drivers. Analyzing things like head orientation, eye movement, yawning, and ocular condition are crucial. This study suggests a mechanism for detecting driver fatigue by keeping track of the number of times the driver opens and closes their eyes and their lips. If the driver keeps his eyes closed for too long, an alarm will go off. In addition, if the driver is seen nodding off more than some few times, the vehicle's owner is contacted by e-mail to confirm that the driver is exercising precautions to prevent dozing off behind the wheel. The results of the system proposed in the paper on Deep learning technology of Dlib show that it is effective at detecting tiredness and thus decreasing the number of accidents that occur while driving. The system uses a CNN (Convolutional Neural Network) as its base algorithm for accurate detection and is implemented in MATLAB environments with a camera mounted. The suggested technique achieved a 99% success rate for video clip input.
Evaluations of Parallelism Testing Methods Using ROC Analysis

Parallelism is often a prerequisite in relative potency determination using bioassays. It involves testing the similarity between a pair of dose–response curves of reference standard and the test sample. Parallelism testing methods currently in use include p-value-based significance tests and interval-based equivalence tests. Recently the conceptual and empirical properties of these tests have been assessed and compared by researchers. However, the evaluations are incomplete due to the lack of a common framework for comparing the two methods. Based on receiver operating characteristic (ROC) curve analysis, we propose that the area under the ROC curve (AUC) be used as an overall figure of merit for parallelism tests. A simulation study is conducted to compare the performance of significance and equivalence tests. We demonstrate that the equivalence test outperforms the significance test if the equivalence limits are properly selected. A method for determining the optimal equivalence limits is suggested. Such optimal equivalence limits potentially make the application of the equivalence test to parallelism testing both practical and feasible.
Influence of Atmospheric Correction Models on the Discriminatrion of Crops using Airborne Hyperspectral Imagery

Crop discrimination is a general application of hyperspectral remote sensing with supervised learning-based classification being the approach of choice. The quality of classification is substantially influenced by the atmospheric impacts on the radiance recorded. The primary goal of atmospheric correction is to extract from remotely sensed data of the target’s surface reflectance or emittance values. We have assessed the influence of different atmospheric correction schemes on the quality of crop discrimination using airborne hyperspectral imagery acquired on a site in the Anand district of Gujarat. The variation of the classification performance as obtained from different machine learning algorithms has been assessed for quantifying the influence of different perspectives of atmospheric corrections on the hyperspectral imagery for crop classification. Results indicate a strong dependence on the classification of crops, especially with the imagery corrected using statistically dominant atmospheric correction methods. Despite marginal differences across the various radiative transfer modelling (RTM)-based models, a general trend of higher-level classification performance in crop classification indicates a uniform distribution of load factors and image quality metrics. Accuracy estimates from the imagery corrected using a semi-empirical model (e.g., QUAC) are only marginally lesser than the radiative transfer models while offering great flexibility in terms of computational requirements and scope for automated image correction. So, at times, an empirical model can also be a better method for atmospheric correction.
Endmember Extraction without prior Number of Endmembers using Barycentric Abundances

Endmember Extraction for Hyperspectral Images requires prior knowledge about the number of endmembers present at the scene. This essential but elusive information is not always available in the case of real scenarios. Moreover depending on techniques exclusively designed to find the number of endmembers may affect the accuracy of extracted endmembers and increases the computational burden. To address these issues in Hyperspectral Unmixing, a novel approach to extract the endmembers without the need for the number of such endmembers is proposed in this paper. The proposed method uses a barycentric abundance estimation technique to find endmember spectra from the input data which are linearly independent. Additionally, to reduce the computational cost and incorporate spatial features, a preprocessing step based on SLIC segmentation is proposed. The method is tested on both Synthetic and Real Hyperspectral images and results show better performance compared to the competing state-of-the-art methods.
Learning Adaptive Probabilistic Models for Uncertainty-Aware Air Pollution Prediction

The air pollution problem has been a serious issue for public health and city development in recent years, which rises an urgent demand for accurate air pollution prediction models. Traditional time series prediction scheme has been challenged in such case because the air pollution signals can be highly dynamic and uncertain. Distinct latent physical processes and complex environment changes over time make it hard for one fixed model to give consistently good performance. Also probabilistic prediction and uncertainty-aware estimation are critical when the model is used for public decisions in practice. However, few previous works can simultaneously provide adaptive and probabilistic air pollution prediction abilities, which are important practical issues for the real-world implementation of data-driven air pollution prediction models. In this paper we propose an adversarial meta learning approach to address these concerns. After reformulating the target signals as a collection of independent but similar mini-tasks along time, we adversarially learn a meta model that includes an implicit generator to adaptively give more informative probabilistic predictions across tasks. We further provide a bayesian meta learning interpretation that recasts the proposed model as an approximate minimizer for the Wasserstein distance between a generative latent model and the true data distribution. Our model provides an algorithm that explicitly models how the prediction distribution is conditioned on underlying data patterns and simultaneously gives adaptive uncertainty estimation. Experiments on both synthetic and real-world air pollution datasets show that the proposed model can simultaneously provide better probabilistic and adaptive prediction while keeping stratifying point prediction error.
Sentiment Analysis of Stock Prices and News Headlines Using the MCDM Framework

In the 21st century, the speedy progress in digital data procurement has led to the fast-growing amount of data kept in the database, data warehouses, or other data storehouses. The main reason behind this is that it provides a fast spreading of information and increases technology usage. The stock market is one of the utmost competitive financial markets where traders want to compute financial capacities with low latency and high output. In this study, we introduced an unsupervised MCDM-based Grey Relational analysis (GRA) model that targets giving appropriate sentiment tags to the news headlines and predicting the forthcoming stock prediction. To check the proposed model's applicability, we used INFOSYS and WIPRO datasets, which give satisfactory results over the proposed model. We recorded an accuracy of around 87%. We utilize a practical GRA model approach to evaluate and recommend the finest share stocks based on news headlines from multiple web sources. Our system's performance is evaluated using real-time data from WIPRO and INFOSYS.
Illumination of reflection nebulosity in the L1551 dark cloud by the infrared sources IRS5 and L1551NE

Les donnees de polarisation lineaire optique indiquent que les nebulosites visibles dans le nuage sombre L 1551 sont vues principalement grâce a la lumiere reflechie. On presente les cartes de la polarisation optique de la nebulosite dans le nuage sombre L 1551 et on identifie les sources d'illumination des diverses regions nebuleuses
Model-Independent Robust Consensus of Multiple Euler–Lagrange Systems

The robustness of multiple Euler–Lagrange systems (MELSs) measures the capability of maintaining coordination control performance when experiencing disturbance or faults. This article investigates a leader–follower robust consensus problem of MELSs, which experience time-varying input disturbance and uncertain communication link faults (CLFs). First of all, based on an adaptive control theory, we design fully distributed observers for estimating the dynamic and state of leader, which have robustness to CLFs. Then, an observer-based proportional–integral (PI) control protocol is designed to achieve consensus of MELSs with robustness to time-varying input disturbance. Different from the existing related results, this robust observer-based PI controller is fully distributed and model independent, which is irrelevant to any prior information (i.e., the structures and features) of agent dynamic or global network information. At last, the validity of the proposed theoretical results is confirmed by a simulation example.
Design of PI/PID Control Algorithm for a Benchmark Heat Exchanger System using Frequency Domain Specifications

This paper presents the design of a proportionalintegral (PI)/proportional-integral-derivative (PID) controller for a shell and tube heat exchanger (STHE) system. In a heat exchanger system, the transfer of heat from a hot fluid to a colder one is maintained by adopting a cascade control structure. The main objective of this work is to maintain the temperature of the shell outlet. The cascade control structure is designed for controlling the temperature in which the inner loop contains a flow controller that controls the flow rate of the tube outlet. STHE system is modeled to a first order plus dead time (FOPDT) system for the inner loop (controlling the flow rate) as well as for the outer loop (controlling the temperature). The inner loop is controlled by a PI controller while the outer loop is controlled by a PID controller. The controllers are designed based on the frequency domain specifications such as gain margin and phase margin to attain the desired control output. The proposed controller can achieve the desired values of temperature irrespective of external disturbances and parameter uncertainties.
Research on Risk Assessment Algorithm for Power Monitoring Global Network Based on Link Importance and Genetic Algorithm

To improve the risk assessment capability of the Power Monitoring Global Network (PMGN), based on the Software Defined Network (SDN) architecture, a Link Importance Evaluation Algorithm (LIEA-ARBR) based on the Active Route and the Backup Route was constructed. The link association risk degree before and after the link failure at different levels was calculated, and the adaptive coefficient was used to fuse the link association risk of the three-layer link, and the link importance degree of the active and backup routes was formed. The simulation results showed that the LIEA-ARBR can more accurately and reliably evaluate the importance of network links whether it was a small or large network, and services were randomly distributed or deterministically distributed, providing a prerequisite for risk assessment and control of PMGN. Taking the network risk as the optimization goal, a routing optimization strategy based on genetic algorithm is formed. The network risk assessment result based on Genetic Algorithm was 37.43% lower than that of the Shortest Path Algorithm; when subjected to simulated network attacks, the CNR of important links was reduced by nearly 20%, and the overall network risk was reduced by nearly 19%.
Strictly Prioritized Multihop Transmission of High-Priority Data Composed of Multiple Frames in Wireless Sensor Networks

Wireless sensor networks and their applications are becoming more diverse, and as an important future challenge, urgent high-priority data must be transferred with minimum delay and loss in a prioritized manner. This paper proposes strictly prioritized multihop transmission of high-priority data, composed of not only single frame but also multiple frames, by reserving the channel and exploiting medium access control coordination among sensor nodes on the route. Its effectiveness is shown through computer simulation.
Multilevel PAM with ANN Equalization for an RC-LED SI-POF System

In this experimental study, data transmission over step-index plastic optical fiber (SI-POF) is implemented with multilevel pulse amplitude modulation (PAM-M) scheme using a resonant cavity light emitting diode (RC-LED) as the optical transmitter. An artificial neural network (ANN) based equalizer is used in the RC-LED SI-POF system to jointly mitigate channel distortion and system non-linearities. Furthermore, the ANN equalizer's performance is compared to state-of-the-art equalizers used for the system - the Volterra equalizer and the decision feedback equalizer (DFE). The ANN equalizer offers the best bit rates compared to the others for the system with high non-linearities. For instance, at a BER of about 10−3, the ANN equalizer results in a bit rate of 780 Mbps, 710 Mbps, and 650 Mbps with PAM-2, PAM-4 and PAM-8 schemes, respectively. However, the Volterra equalizer results in a bit rate of 720 Mbps, 660 Mbps, and 580 Mbps with these PAM schemes, respectively. And with the DFE, the bit rates are 610 Mbps, 510 Mbps, and 120 Mbps, respectively.
Information Constrained Optimal Transport: From Talagrand, to Marton, to Cover

The optimal transport problem studies how to transport one measure to another in the most cost-effective way and has wide range of applications from economics to machine learning. In this paper, we introduce and study an information constrained variation of this problem. Our study yields a strengthening and generalization of Talagrand’s celebrated transportation cost inequality. Following Marton’s approach, we show that the new transportation cost inequality can be used to recover old and new concentration of measure results. Finally, we provide an application of this new inequality to network information theory. We show that it can be used to recover almost immediately a recent solution to a long-standing open problem posed by Cover regarding the capacity of the relay channel.
Classification of Blueberry Varieties Based on Improved EfficientNet

A method to classify blueberry varieties based on an improved EfficientNet is proposed, aiming at the high cost of manual classification and the difficulty of manual confirmation of new varieties. First, considering that there are few datasets of blueberry leaf samples available on the internet, blueberry leaf samples from different seasons are collected and photographed, and four blueberry leaf image datasets are established, including germination, maturity, dormancy, and mixed stages. Second, to better allocate feature weights, an improved EfficientNet is introduced to generate a blueberry variety classification model. Finally, experiments on different datasets are conducted using different models for blueberry variety classification. For the recognition results of the dormant-stage dataset, the accuracy rate of the improved EfficientNet method reaches 99.85%, exceeding EfficientNet-B1 by 3.05%. For the recognition results of datasets of the germination, maturity, dormancy, and mixed stages, the accuracy rates of the improved EfficientNet method are 98.50%, 99.65%, 99.85%, and 97.92%, respectively. These results show that the improved EfficientNet can be applied to blueberry variety recognition with a high recognition rate, and it has a certain robustness to different periods of leaves. This research can provide new inspiration and technical support for blueberry variety recognition.
Cardiac Disease Prediction for Heart Monitoring using Data Mining Techniques

There are millions or tonnes of data relating to the healthcare and medical sectors, and these data hold a wealth of hidden information. Authors can use this knowledge to their advantage in their connected industries by using it to inform their judgments. There are several highly sophisticated methods and approaches for mining and making effective judgments with the information that has been successfully collected. Coherence Cardiac Risk Prediction System (CCRPS), a highly successful system for the early detection or forecasting of different levels of cardiac disease risk, was created utilizing neural networks. The training approach used in this work is a multilayer back propagation neural network for perception. The study's objective is to apply artificial intelligence (AI) to the diagnosis of heart illness in both pathological and normal contexts. Data mining-based approaches are commonly used, and many different AI technologies are anticipated to be used. The investigation's conclusions show that the UCI Datasets have an accuracy of 99.77 percent.
A Analysis of Artificial Neural Networks use in Predicting Secondary Structures

Knowing the roles of proteins is crucial given the growing significance of proteins in daily life. Understanding protein structure explains how proteins work. The experimental methods used to analyse protein structures are labor-intensive, expensive, and highly skilled. Therefore, it is crucial to design a workable quantitative strategy. This area of study focuses on protein-secondary-structure predictions, that is strongly compared to its tertiary structure, due to the tremendous complexity of directly predicting protein structure (tertiary structure). In this study, a range of features, such as position-specific scoring matrices, hidden Markov model alignment factorization, and physicochemical factors, are studied in order to anticipate the secondary structure. Additionally, it tries to investigate a good mix of traits that could capture various details about the protein secondary structure. The models are then fitted with an artificial neural network, and evaluation measures such as accuracy, specificity, recall, f1-score, and running time are compared across different datasets. Our suggested model outperforms the benchmark models by at least 2.9% when tested on the CB513 dataset and CB6133 dataset, respectively.
Interactions entre l'analyse complexe et la théorie des opérateurs

We give necessary conditions and sufficient conditions for sequences of reproducing kernels (kΘ(·, λn))n≥1 to be overcomplete in a given model space K Θ where Θ is an inner function in H ∞, p ∈ (1,∞), and where (λn)n≥1 is an infinite sequence of pairwise distinct points of D. Under certain conditions on Θ we obtain an exact characterization of overcompleteness. As a consequence we are able to describe the overcomplete exponential systems in L(0, a). Mathematics Subject Classification (2000). Primary 46E22, 46B15.
A Machine Learning Approach to Detect Parkinson Disease Using Speech Signals

One of the leading neuro-degenerative disorders is Parkinson's disease (PD), which affects a large number of people worldwide. Diagnostics of PD are complex because a detailed clinical review of the patient's medical record is necessary. One of the characteristic and predictive indicators of this disease is voice. Almost all such patients experience at least some vocal degeneration; therefore voice data is an effective way to diagnose the disease. This paper presents a method for analyzing voice impairments by means of a system design in people who have PD and in people who are not afflicted with the disease. Various methodologies including Artificial Neural Networks, Logistic Regression, Decision Trees, SVM, and Random Forests were applied in this study. To test how well these algorithms can discriminate between PD and healthy subjects, the best accuracy predictor model can be determined out of this implemented model. With an accuracy of 88.81%, neural network ended up being the best algorithm among all of the algorithms used during this study.
Effectiveness of Transfer Learning, Convolutional Neural Network and Standard Machine Learning in Computer Vision Assisted Bee Health Assessment

Honeybees are vital to society, as they pollinate over 80% of plants. Unfortunately, honeybee colonies have been losing at an average rate of 39.7% per year. Beehive monitoring depends on human visual examinations, which is time consuming and disruptive to colonies. It is critical to apply an effective and efficient technique to monitor bee health and save bee colonies. The paper provides a systematic study of applying transfer learning, classic convolutional neural network (CNN) and standard machine learning models on image-based bee health classification. Five models (SVM, CNN, VGG19, InceptionV3, MobileNet) have been evaluated on a real-world dataset with more than 5000 bee images and six health sub-classes. The accuracy rates from all five models were above 90% in the test dataset. In particular, VGG19 and CNN achieved 98.65% and 96.91% accuracy, respectively. These accuracy rates were higher than the best accuracy rate of 95% in other previous research. The promising model results demonstrate the potential of applying AI techniques to build an intelligent beehive inspection system.