Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of a UWB Antenna Utilising Fractal Geometry and EBG Structures for Wireless Applications

The analysis of fractal antenna designed for ultra wide band (UWB)characteristics are presented in this work. To enhance the antenna's radiating properties, fractal technology is used. The suggested fractal antenna has a 9 GHz impedance bandwidth with a range of 3 GHz to 12 GHz, a maximum gain of 3 dB, and an efficiency of 89%. Electromagnetic bandgap structures are integrated into the antenna and shorted at the ground in order to improve band rejection characteristics in the WLAN band and create band notched characteristics in the WLAN and satellite downlink band. In order to analyse the antenna, CST Microwave Studio is used.
Analyzing Multimodal Datasets for Detecting Online COVID Misinformation: A Preliminary Survey Study

Amidst the recent COVID pandemic, the extensive spread of misinformation has led to such significant societal ramifications that the World Health Organization has termed this issue as an “infodemic”. Addressing this COVID infodemic problem requires careful analysis and correct interpretation of the COVID related information claims. While this research need has led to the creation of multiple datasets for aiding COVID misinformation detection, these current datasets primarily include textual data to serve this purpose. Prior works involving these datasets has made limited use of the implicit graphic image contents in this regard. Existing literature indicates a lack of proper utilization of the valuable insights that can be derived from the visual cues found in online COVID articles, sites, and social media posts. In order to address this research gap, we perform a preliminary analysis of three different multimodal COVID datasets, which are traditionally used for COVID information claim processing. We specifically collect the images plus infographic elements from the online websites listed in these datasets, and come up with five notable categories for these COVID visual cues while studying them. To our knowledge, this initial survey study is a first of its kind that investigates multimodal COVID datasets, and leads to a unique collection of COVID image artifacts. This paper produces a strategic taxonomy of these various COVID visual cues as an important contribution, and makes an effort to advocate for the need of explicitly multimodal datasets that account for both textual and image data to successfully analyze COVID information claims.
Training A Deep Reinforcement Learning Agent for Microgrid Control using PSCAD Environment

The accessibility of real-time operational data along with breakthroughs in processing power have promoted the use of Machine Learning (ML) applications in current power systems. Prediction of device failures, meteorological data, system outages, and demand are among the applications of ML in the electricity grid. In this paper, a Reinforcement Learning (RL) method is utilized to design an efficient energy management system for grid-tied Energy Storage Systems (ESS). We implement a Deep Q-Learning (DQL) approach using Artificial Neural Networks (ANN) to design a microgrid controller system simulated in the PSCAD environment. The proposed on-grid controller coordinates the main grid, aggregated loads, renewable generations, and Advanced Energy Storage (AES). To reduce the cost of operating AESs, the designed controller takes the hourly energy market price into account in addition to physical system characteristics.
Using Physiological Metrics to Improve Reinforcement Learning for Autonomous Vehicles

Thanks to recent technological advances Autonomous Vehicles (AVs) are becoming available at some locations. Safety impacts of these devices have, however, been difficult to assess. In this paper we utilize physiological metrics to improve the performance of a reinforcement learning agent attempting to drive an autonomous vehicle in simulation. We measure the performance of our reinforcement learner in several aspects, including the amount of stress imposed on potential passengers, the number of training episodes required, and a score measuring the vehicle's speed as well as the distance successfully traveled by the vehicle, without traveling off-track or hitting a different vehicle. To that end, we compose a human model, which is based on a dataset of physiological metrics of passengers in an autonomous vehicle. We embed this model in a reinforcement learning agent by providing negative reward to the agent for actions that cause the human model an increase in heart rate. We show that such a “passenger-aware” reinforcement learner agent does not only reduce the stress imposed on hypothetical passengers, but, quite surprisingly, also drives safer and its learning process is more effective than an agent that does not obtain rewards from a human model.
SIA-Net: Scalable Interaction-Aware Network for Vehicle Trajectory Prediction Based on Self-Attention

In order to navigate through different scenarios safely and efficiently, self-driving vehicles must predict future trajectories of other vehicles, which is a challenging task due to the implicit vehicle interactions in the driving scenario. Because there is no predefined number of surrounding vehicles, the model must be scalable to cope with scenarios of different vehicle numbers with high accuracy and low computation cost for jointly predicting future trajectories of all vehicles. However, previous methods mainly focus on predicting a single trajectory of the target vehicle, which makes them subject to accuracy and computation speed. In this paper, we propose SIA-Net that predicts future trajectories of all vehicles in the scenario independent of the vehicle number. SIA-Net learns the implicit interactions of all vehicles by self-attention social pooling and generates each trajectory through one forward propagation by attentional decoder. Experiments demonstrate the improvement of our model in prediction accuracy on the publicly available NGSIM and INTERACTION datasets while keeping the computation cost low. We also present qualitative analysis to study the mechanism of our model.
Wheat Yield Prediction using Temporal Fusion Transformers

In precision framing, Machine Learning models are an essential decision-making tool for crop yield prediction. They aid farmers with decisions like which crop to grow and when to grow certain crops during the sowing season. Many Machine Learning algorithms have been used to support agriculture yield prediction research, but it is observed that Deep Learning models outperform the benchmark Machine Learning algorithms with a significant difference in accuracy. However, though these Deep Learning models perform better, they are not preferred or widely used in place of Machine Learning models. This is because Deep Learning methods are black box methods and are not interpretable, i.e., they fail to explain the magnitude of the impact of the features on the output, and this is unsuitable for our use case.In this paper, we propose using Temporal Fusion Transformer (TFT), a novel approach published by Google researchers for wheat yield prediction viewed as a Time Series Forecasting problem statement. TFT is the state-of-the-art attention-based Deep Learning architecture, which combines high-performance forecasting with interpretable insights and feature importance. We have used TFT to perform wheat yield prediction and compare its performance with various Machine Learning and Deep Learning algorithms.
Multitask Learning Neural Networks for Pandemic Prediction with Public Stance Enhancement

State and local governments have imposed health policies to contain the spread of COVID-19 since it had a serious impact on human daily life. However, the public stance on these measures may be time-varying. It is likely to escalate the infection in the area where the public is negative or resistant. To take advantage of the correlation between public stance on health policies and the COVID-19 statistics, we propose a novel framework, Multitask Learning Neural Networks for Pandemic Prediction with Public Stance Enhancement (MP3), which is composed of three modules: (1) Stance awareness module to make stance detection on health policies from users' tweets in social media and convert them into a stance time series. (2) Temporal feature extraction module that applies Convolution Neural Network and Recurrent Neural Network to extract and fuse local patterns and long-term correlations from COVID-19 statistics. Moreover, a Stance Latency-aware Attention is proposed to capture dynamic social effects and fuse them with temporal features. (3) Multi-task prediction module to adopt Graph Convolution Network to model the spread of pandemic and employ multi-task learning to simultaneously predict COVID-19 statistics and the trend of public stance on health policies. The proposed framework outperforms state-of-the-art baselines on both confirmed cases and deaths prediction tasks.
Compact Low-pass Filtering-response Wilkinson Power Divider with Wide Harmonic Suppression

In this article, a compact Wilkinson filtering power divider (FPD) operating at a frequency of 2.25 GHz with low-pass behaviour and a wide attenuation band is presented for LTE communications. The microstrip structure is modelled by using T - shaped stub resonators loaded symmetrically in the microstrip line to achieve a low-pass filtering response. The lowpass filter operates at a 2.4 GHz cut-off frequency with 0.35 dB insertion loss, and 62 dB/GHz roll-off factor (ROF). Two defected ground structures (DGS) of dissimilar shapes are incorporated in the ground plane to attain wide attenuation band performances up to 14.6 GHz with an attenuation level of -20 dB, and a relative stopband rejection of almost 144 %. Equal power division is attained by embedding the stub-loaded microstrip structure in the traditional circular-shaped Wilkinson power divider (WPD). Good isolation can be obtained by connecting a 100 Ω isolation resistor (IR) amid the two output ports. For experimental validation, the lowpass filter and FPD designs are fabricated and measured.
Multiclass Artificial Neural Network based Facial Expression Recognition

Facial emotion recognition has emerged as one of the important research area from past few decades. It has wide variety of applications such as marketing research, medical field, security, criminal interrogations, etc. This paper provides a deep learning approach for face and emotion recognition that may be used to create a system that can accurately identify and recognize people. Computer vision can find and recognize the human face and its emotions along with the numerous algorithms but to get the best results we are using a deep learning technique. The task is divided into three parts, the first of which is the detection of a human face from a camera, and the second of which is the analysis of the acquired data. Keras convolutional neural network was used to support the features and database used. Human faces are validated in the last phase to categorize human emotions such as Happy; neutral, furious, sad, disgusted, and surprised are all emotions that can be felt ising multiclass artificial neural network approach. Face detection, identification, and emotion categorization are the three main aims of the proposed study. For computer vision approaches, the Open CV library, dataset, and python programming are employed in support of this work.
A comparitive analysis on various machine learning techniques for breast cancer detection

The primary goal of the study is to detect breast cancer patients at an early stage. It is the mass elegant deadly diseases compared to rest of the infections in medical field. Breast cancer was one of the reasons for dying among women globally on report of the WORLD HEALTH ORGANIZATION (WHO). This research seeks to classify tumors based on data attributes as malignant (cancerous) tumors that penetrate and destroy healthy body tissues or benign tumors that do not (non-cancerous). We are using a support vector machine (SVM) classifier, random forest (RF) classifier & decision tree classifier for breast cancer detection by training, testing, and splitting its attributes. For detecting the breast cancer we are using the Wisconsin Breast Cancer Dataset (WBCD) data obtained out from UCI library. The database composed of 569 analyses, out of which 357 are benign (non-cancerous), 212 are malignant (cancerous). The comparative analysis of support vector machines (SVM) is done for finding the accuracy. The method gives good results for the data of training. Moreover, the approach attains accuracy 96.50% by Support Vector Machine, 97.12% by Random Forest Classifier and 99.50% by Decision tree Classifier.
Recent Security Challenges in Agriculture Applications in Smart Agriculture: A Survey

Smart agriculture is one of management concept mainly meet out on providing the agricultural industry with the available resources to get the upgraded technology services – including big data, the cloud computing and the internet of things for the tracking, monitoring, automating and analyzing operations. This operations we have lot of applications in market. Many peoples are download this applications from play stores in online and use these mobile applications offline to get the access business assets or perform particular business functions. And unfortunately, many of the applications have little or no security guarantees. They are easily exposed to attacks and violations of business security policies all the time.so in my paper I identified some of the security features needed for proper agriculture applications. The security checklists are enforce strong authentication, encrypt mobile communications, patching applications and operating system bad vulnerabilities, protect against device theft, scan mobile apps for malware, protect app data on your device, secure the platform, isolate application information. On this checklist am prepared the survey report and applies for further.
Novel Protein Structure Prediction Model Using Fused Pipit Adapted Deep Convolutional Neural Network Classifier

One of the main objectives of computational biology is protein structure prediction. This is important for the development of novel enzymes in biotechnology and medicine. Each protein has a certain shape and structure and our life is supported by the complex and coordinated interaction of proteins. Hence identifying the protein structure possesses various challenges and various researches are performed relying upon various classifiers. In this research a fused pipit adapted deep convolutional neural network classifier (CNN) is used for the detection of the PSS with higher accuracy. The feature extraction is made using the fused Natural language processing (NLP) based pretrained models that efficiently extracted the features and is developed using the pretrained models T5XLuniref and XLnet model. The pretrained and the deep CNN classifier is optimized effectively using the pipit optimization that mimics the foraging and the safeguarding behavior of the pipits. The enabled optimization aids in the tuning of the fusion parameters and the hyper parameters of the classifier. By measuring the improvement, the model's dominance is demonstrated, and suggested method attained an progress of 1.77 %for accuracy, 1.01 % for sensitivity and 6.90 % for specificity, which proves the efficacy of the model.
A Low Parametric CNN Based Solution to Efficiently Detect Brain Tumor Cells from Ultrasound Scans

Implementation of deep neural networks in medical imaging of brain cells to identify tumors is shaping up to be a reliable approach in medical science. Early and accurate detection of brain tumor cells is critical for effective patient treatment. Initially, this paper demonstrates three widely used CNN models, VGG16, ResNet50, and MobileNet, to identify tumor cells in brain MRI images with an accuracy of 97%, 94.5%, and 99% respectively. The dataset used for this study contains brain MRI scans called Br35h. The purpose of this research is to develop a modified CNN model to attain similar performance statistics to widely accepted CNN models and extract all meaningful and precise information from images with the least amount of error possible while maintaining greater run-time efficiency. The proposed model achieved an overall classification accuracy of 98.5%. Finally, the model's performance is also compared with that of the previously stated models, and it is observed that it performs on par, if not better, than those models despite having a fraction of the total parameters. This study aims to contribute to the computer-aided diagnostic (CAD) system by implementing the proposed model with relatively fewer computational power requirements.
Speech Emotion Recognition from Audio Files Using Feedforward Neural Network

Speech Emotion Recognition (SER) is an act of recognizing human emotion and affective states from speech. This is because voice often reflects underlying emotion through tone and pitch. Emotion recognition is a component of speech recognition which is gaining more popularity and need for it is increasing exceptionally. Our paper aims to use the Feedforward Neural Network to recognize emotions from unseen data (i.e., audio files) and label them according to the range of different emotions using appropriate variables (such as modality, emotion, intensity, repetition etc.) found in the data. This approach can be particularly useful in the case of identifying the inherent emotion behind human voice.
Cloud-Based Big Data Analytics on IoT Applications

The high growth of the data produced in the last decades and the increase in its rapid consumption is associated with significant increase of the smart objects. However, these IoT applications generate data that will not be useful without the involvement of analytics to generate value and intelligent insight. This paper will review studies on cloud-based IoT applications and data analytics that exist in the literature from different perspectives. Moreover, this study adds value by providing comparisons directed towards the different IoT platforms, IoT data analytics techniques, transmission protocols, applications domain, dataset, results, and implementation environments. Finally, we partitioned the literature based on domain factors, and presented several industry trends for popular platforms.
Tracking Periodic Voltage Sags via Synchrophasor Data in a Geographically Bounded Service Territory

Existing methods for synchrophasor data analysis focus on oscillations that can be interpreted as a sum of sinusoids with or without damping, however, they are more difficult to apply when considering other types of waveforms. This paper investigates how to apply spectral estimation analysis methods when analyzing a periodic voltage sag whose waveform is comparable to a periodic pulse train. A methodology is proposed where the estimated power spectral density and spectrograms are used to detect the area impacted by a periodic sag and to identify its dominant propagation path, which helps with the localization of the disturbance’s spread. The proposed methodology is applied to measurement data obtained from a region in Dominion Energy’s service territory that is geographically bounded by other utilities, which validates the effectiveness of the proposed method in a real-world utility setting.
Morse-Bott functions on manifolds with semi-free circle action

1. S1-invariant Morse-Bott functions Let W 2n be a closed smooth manifold. Suppose that W 2n admits a smooth semi-free circle action with finitely many fixed points. It is known that every isolated fixed point p of a semi-free S1-action has the following important property: near such a point the action is equivalent to a certain linear S1 = SO(2)-action on R2n. More precisely, for every isolated fixed point p there exist an open invariant neighborhood U of p and a diffeomorphism h from U to an open unit disk D in Cn centered at origin such that h conjugates the given S1-action on U to the S1-action on Cn with weight (1, . . . , 1). We will use both complex, (z1, . . . , zn), and real coordinates (x1, y1, . . . , xn, yn) on C n = R2n with zi = xi+ √ −1yi. The pair (U, h) will be called a standard chart at the point p. Let f : W 2n → R be a smooth S1-invariant function on the manifold W 2n. Denote by Σf the set of singular points of the function
Face Recognition Based on Point Cloud Data Captured by Low-cost mmWave Radar Sensors

The image based face recognition is a well-established technology nowadays. However, commercially available cameras generally perform poorly in environments with low visibility such as in dark night or foggy weather. In addition, reliable privacy protection to avoid potential data leakage poses quite a challenge for resource-constrained Internet of Things (IoT) devices at edge, particularly for cameras producing visual data. As a promising alternative, we propose to use commercial low-cost mmWave sensors to assist or perform completely the face recognition task. In our approach, we directly leverage the point cloud data captured by an off-shelf mmWave radar sensor to train a neural network modified based on PointNet. Based on our modified PointNet architecture, we are able to achieve an overall accuracy of 98.69%, with much lower computation and data bandwidth requirement as compared with the image based approach. We believe our work serves as a great example of the potential of using mmWave sensors for a richer understanding of the sensing environments.
Multiscale Approach in Deep Convolutional Networks for Minutia Extraction from Contactless Fingerprint Images

Biometric identification by contactless fingerprinting has been a trend in recent years, reinforced by the pandemic of the new coronavirus (COVID-19). Contactless acquisition tends to be a more hygienic acquisition category with greater user acceptance because it is less invasive and does not require the use of a surface touched by other people as traditional acquisition does. However, this area presents some challenging tasks. Contact-based sensors still generally provide greater biometric effectiveness since the minutiae are more pronounced due to the high contrast between ridges and valleys. On the other hand, contactless images typically have low contrast, so the methods fail with spurious or undetectable details, demonstrating the need for further studies in this area. In this work, we propose and analyze a robust scaled deep learning model for extracting minutiae in contactless fingerprint images. The results, evaluated on three datasets, show that the proposed method is competitive against other minutia extraction algorithms and commercial software.