Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on registration algorithm of photovoltaic panel based on 3D LiDAR point cloud

Photovoltaic Panel Modeling is one of the key technologies for monitoring the state and evaluating the performance of photovoltaic power systems. In the process of 3D LiDAR point cloud modeling, point cloud registration plays a crucial role. To address the limitations of the traditional Iterative Closest Point (ICP) algorithm, such as high requirements for initial position, slow computation speed, and lack of robustness in point-to-point correspondence search, an optimized method is proposed by combining the Four-Point Congruent Sets (4PCS) algorithm and the Generalized Iterative Closest Point (GICP) algorithm. This approach incorporates three-dimensional voxel grid downsampling and bidirectional K-Dimensional tree (KD-tree) for accelerated nearest neighbor point search, enabling efficient and accurate registration of photovoltaic panel point clouds. Experimental evaluations using simulated data and actual photovoltaic panel scans with LiDAR demonstrate significant improvements in registration accuracy and computational efficiency compared to the traditional ICP algorithm.
X/Ku-Band Highly-Isolated Circularly Polarized Horn Antennas for Full Duplex Applications

A simultaneous transmitting and receiving antenna system, consisting of two horn antennas operating at X/Ku band, is proposed for wideband full-duplex applications. Each horn antenna has circular aperture. Four ridges are inserted to decrease the working frequency and increase the bandwidth. The two horn antennas are embedded in parallel in a cylinder cavity. Corrugation slots are etched around the circumference of the cylinder cavity to increase the port isolation. The VSWR in the 8–18 GHz band is less than 2, and the peak gain is above 15 dBiC in the whole band. What's more, the port isolation between the transmitting and receiving horns is higher than 70 dB, with element spacing of 350 mm.
Research on Channel Modeling in Enclosed Metal Scenario with Aperture

This paper proposes a method for extracting channel features in specialized 5G application scenarios, aiming to address the simulation discrepancies in metal aperture-coupled models using the ray tracing algorithm. Simulations performed on a 100 × 100 × 15 cm3 metal cavity with aperture using both full wave and ray tracing methods revealed distinct differences in field strength distributions. Based on the derived convolution formula, this study presents a more accurate electric field distribution in metal cavity with aperture. Compared to the straightforward ray tracing method, the proposed channel modeling and field strength extraction techniques offer a novel theoretical foundation for electromagnetic wave propagation in enclosed metal scenarios. Within this metal model, the proposed method combines the precision of full wave methods with the efficiency of ray tracing techniques.
Study on GNSS Spoofing Interference Detection Method in Urban Multipath Environment Based on CNN and Clustering Model

Global Navigation Satellite Systems (GNSS) provide essential positioning, navigation, and time (PNT) information for a wide range of civil and military applications across various scenarios. However, GNSS signals are vulnerable to different forms of interference, particularly spoofing interference. In multipath environments, detecting spoofing interference becomes even more challenging and inefficient. In response to the challenges related to the degradation of spoofing interference detection performance in multipath environments, we propose a novel algorithm named GNSS-CC, which combines the Global Navigation Satellite System (GNSS), Convolutional Neural Network (CNN), and Clustering for the purpose of detecting both multipath interference and spoofing interference. The method leverages a combination of CNN and Clustering technology to construct a model, effectively utilizing distinguishing features between the correlation peaks generated by multipath interference and spoofing interference, enabling accurate classification and identification of multipath and spoofing signals. In this study, various machine learning algorithms are compared, and the simulation results show that GNSS-CC exhibits superior performance, achieving a detection rate of 95.8% for GNSS spoofing interference detection in multipath environments.
Sentiment Analysis on Online Social Networking Data for the Identification of Depression Using Several AI Techniques: A Literature Review

Emotional analysis is a growing trend today to recognize the feelings of people in many circumstances in life. Data from online platforms will be used throughout the whole procedure, which involves an analytical and classification-based processes, and is composed of textual data, emoticons, and other types of data. In previous studies, multi-class classification offers more correct information than both ternary and binary classification. In a multiclass classification, information separated in to numerous subcategories depending on polarities. Techniques such as deep learning and algorithms of machine learning will be utilized in the process of categorization. Social media can be used for monitoring and exploring emotional states. This study presents a description of emotional analysis of publicly available data by applying different artificial intelligence approaches to identify anxiety or frustration. The study optically demonstrated that emoticons, social media texts, and data including emoticons were used to determine emotions using various intelligent approaches. Analysis results with multiclass classification using a deep learning techniques or models are more accurate.
Parkinson's Disease Prediction using XGBoost and SVM

Parkinson's disease or paralysis agitans or shaking palsy is a nervous condition that limits mobility. The condition may result in tremors, stiffness, or sluggishness of movement. Although there is no known treatment for this illness, the symptoms can be markedly reduced with medicines. To increase accuracy, this paper aims to forecast Parkinson's disease using machine learning methods like Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost). These algorithms perform better when used as classifiers, leading to higher accuracy. Hence using these algorithms, the machine learning model can easily predict Parkinson's disease. The accuracy of extreme gradient boosting was 92% and that of the support vector machine was 87%. XGBoost has a built-in capability to handle missing values and it provides various features. It is also effective with large datasets. Support Vector Machines outperform other classification algorithms in terms of performance and speed. It determines the best hyperplane for accurately classifying data points between several classes. The major goal of this study is to more accurately and effectively forecast Parkinson's disease so that it can be treated at the right time to minimize subsequent dangers. More Parkinson's disease cures and higher survival times result from early diagnosis. After detecting the disease, it can be cured through physical treatments and sometimes through surgery or in some cases through medications. It is observed that people diagnosed with Parkinson's disease are of an average age of 60, men are most likely to have this disease and it advances with the age of a person. The progress of Parkinson's disease is usually slow. The symptoms vary from person to person but are usually between a year or two.
Efficient Online Multi-Objective Antenna Optimization

This article proposed an efficient multi-objective methodology for antenna optimization, comprising two main components: an offline initialization module(OIM) and an online model optimization module(OMOM). The OIM primarily focuses on data sampling and initializing the database. Specifically, in the OMOM, the transfer function model serves as a proxy model to reduce the time for traditional full-wave simulation calculations, and this module integrates multi-objective optimization(MOO) using genetic algorithm. The validity and efficiency of this proposed model are confirmed with a Fabry-Perot resonator antenna example. Numerical results substantiate the superior performance of the proposed methodology.
A Two-Dimensional Beam Scanning 60 GHz Antenna Array Based on SIW Butler Matrix

A two-dimensional (2-D) scanning 60 GHz multibeam antenna arraybased $4\times 4$ Substrate Integrated Waveguides (SIW) Butler matrix (BM) is proposed. The antenna is composed of a dual series fed patch antenna subarray and 2 groups $4\times 4$ Butler matrices. Butler matrices are arranged in the input ports at both sides of the antenna sub arrays. The 2-D multibeam antenna is achieved a wideband with 15% and maximum gain of 18.8 dBi at 60 GHz. When the 4 ports on the same side are fed, the beam is directed (30°, 30.1°), (−55°, 50.5°), (55°, 50°), and (−30°, 30°). When fed by the four ports on the other side, the beam direction (30°, -30.1°), (−55°, -50.5°), (55°, - 50.5°), (−30°, -30.1°).
Assessing the Feasibility of a Machine Learning-Based Diabetes Prediction Framework for e-Health Systems

In the global landscape of health concerns, diabetes stands out as a pervasive chronic illness marked by elevated blood sugar levels. Its unchecked progression poses significant risks, including cardiovascular complications, visual impairment, gastrointestinal issues, oral health problems, neuropathy, kidney disorders, hypertension, and disruptions in internal bodily functions. This research manuscript delves into the realm of diabetes diagnosis, employing advanced machine-learning techniques. The study meticulously evaluates twelve distinct algorithms, encompassing Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), MLP Classifier (MLP), Gradient Boosting Classifier (GB), AdaBoost Classifier (ADA), Naïve Bayes (NB), Cat Boost Classifier (CAT), Gaussian Process Classifier, and Light GBM (LGBM). The research not only investigates the predictive models but also explores the fundamental mechanisms leading to diabetes development. By leveraging a fusion of machine learning and ensemble methodologies, our objective is to accurately forecast diabetes, thereby enabling early intervention and effective disease management. The outcomes underscore the exceptional performance of the Light Gradient Boosting technique, achieving an impressive accuracy rate of 99%. Through an exhaustive comparative analysis, Light Gradient Boosting emerges as the standout method, surpassing other machine learning strategies in terms of accuracy. This research significantly advances our understanding of diabetes prediction utilizing machine learning and emphasizes the critical importance of early detection in managing this debilitating condition. The insights presented herein pave the way for the development of more robust and precise diabetes prediction models, with the potential to positively influence clinical decision-making and enhance patient outcomes.
A New Type of Solid Plasma Device Silicon Based Backfed PINIP Diode Array

A silicon-based back-fed solid plasma generator PINIP diode array is designed based on semiconductor technology. The diode array connects the solid plasma working area, isolation area and DC control area through silicon-based backfed copper pillars. This structure can realize vertical out-of-plane connection between the solid plasma region and the DC control system. The array was designed and processed through semiconductor technology. Tests have shown that the device has good working characteristics, providing a feasible solution for high integration and two-dimensional structural expansion of solid plasma technology.
Accurate Classification of Ovarian Cyst Using Topic Modeling

Ovaries are an essential female organ in the reproductive and endocrine system, responsible for healthy development and foster fertility. Occasionally, they are affected by vesicles that are filled with fluid and are referred to as cyst. Most of the ovarian cysts are benign in nature, but in few cases, they can be harmful for the patient and need to be treated at an early phase. Globally, ovarian cyst is a prevalent problem among females. The identification and classification of ovarian cyst is a major task in medical data analysis for healthcare providers. Manual classification of ovarian cyst may lead to delay in the diagnosis of the patient. In this paper, three Topic Modeling algorithms are utilized to classify ultrasound textual reports into cystic and normal ovary reports. The results of the algorithms are analyzed on the basis of six evaluation metrics like, accuracy, precision, recall, etc. Latent Dirichlet Allocation with accuracy of 90.71% is proven to be a better algorithm for ovarian cyst classification compared to Non-negative Matrix Factorization and Latent Semantic Analysis with accuracies of 83.79% and 90.34% respectively.
InsDetecter: An Insulator Detection Algorithm Based on Foreground Focusing and Multi-Scale Interaction

In recent years, the intelligent inspection of transmission lines using drones has become increasingly important to maintain the growing total length of transmission lines. However, the complex terrain of transmission lines makes it challenging to distinguish between the foreground and background in drone imagery. Additionally, the significant scale variations in drone imagery result in variations in the features of insulators. To address these issues, we propose an improved insulator detection method based on YOLOv5. First, we introduce a Global Channel Interactive Attention (GCIA) module to establish global channel interdependencies, enhancing the semantic feature representation of insulators and enabling better differentiation from complex backgrounds. Second, we propose a Skip Feature Interaction Fusion (SFIF) module to fuse deep semantic information with shallow edge information, correcting the feature representation of insulators at different scales. In comparison to state-of-the-art models, our proposed method InsDetecter demonstrates strong accuracy and speed on the CPLID and STNPLAD datasets.
Physics-guided Deep Learning for Distribution Network SE

With the rapid advancement of modern power systems, the structure and operation of the grid have become increasingly complex, demanding higher levels of real-time accuracy and efficiency in state estimation (SE). To address the limitations of traditional SE methods for distribution networks, which often rely heavily on model precision, exhibit lower estimation accuracy, and have slower response times, a novel approach called Physics-Guided Temporal Convolutional Network (PGTCN) is proposed. The PGTCN method combines the strengths of data-driven techniques with the physical advantages of model-driven approaches. It begins by training a Temporal Convolutional Network (TCN) model using historical operational data from the distribution network. The model’s predictions of the state variables are then integrated into the power flow equations, allowing for the examination of their consistency with the underlying physical relationships. Through this process, the PGTCN model achieves synergy between data-driven and model-driven methodologies, resulting in higher accuracy and improved estimation precision. Simulations conducted on a three-phase unbalanced distribution system with IEEE 14 nodes demonstrate the superior performance of the proposed SE method. Overall, the PGTCN approach offers a promising solution to the challenges posed by the ever-evolving landscape of modern power systems, promptly ensuring robust and accurate SE.
Automated Feature Interaction and Feature Representation Learning of Multi-field Categorical Data

Categorical data across diverse domains has been extensively employed, encompassing areas such as online advertising, recommendation systems, and internet search. Conventional approaches, which represent it as a binary feature in a high-dimensional space using one-hot encoding, encounter significant data sparsity challenges. Therefore, feature embedding technique is required. FM (Factory Machine), recognized for its efficacy in feature embedding, struggles to effectively uncover intricate high-order patterns. This study introduces a novel Cat2vec-based Factory Machine (CFM) designed to acquire distributed representations of multi-field categorical data. The model employs an Embedding Layer for extracting hidden vectors from initial features, an Interaction Layer + K-Max Pooling Layer for automatic feature interaction and the capture of significant high-order interactions, and finally, an FM Layer to determine the model’s loss function. Empirical findings on an extensive public CTR prediction dataset showcase the superior performance of CFM over several robust benchmarks.
Contextualized Word Embeddings via Generative Adversarial Learning of Syntagmatic and Paradigmatic Structure

In this work, we propose a general regularized function-learning framework of contextualized word embeddings, named Generative Adversarial Learning of Syntagmatic and Paradigmatic structure of Context (GAL-SPC), aiming to build word embeddings via the trainable encoder function based on the distributional structure. What makes a difference is the context of a given word is viewed as a connected graph so that the context-level syntagmatic structure and the document-level statistical pattern can be formalized as a vocabulary co-occurrence matrix. Meanwhile, to better integrate the paradigmatic structure, GAL-SPC exploits posteriori approach based on edit distance and prior approach based on knowledge base to pick paradigmatic contexts with substitutable words and regularizes the training of autoencoder via generative adversarial learning process to better model the syntagmatic and paradigmatic structure of contexts. Finally, we trained GAL-SPC with a public wikipedia corpus, and the intrinsic evaluations with word analogy and similarity tasks and the extrinsic evaluations with document clustering and classification tasks demonstrates that GAL-SPC outperforms other comparative methods.
A Review on the Importance of Internet of Things in Agriculture Applications

The Internet of Things (IoT) is an interconnected set of devices that interact with one another over wired or wireless networks. IoT is an invaluable tool and can be used to improve crop yield throughout the year. The Internet of Things (IoT) has sparked a slew of new applications in various fields. Due to increased efficiency and scalability, the recent push to use IoT technologies benefits the agriculture sector significantly. In this study, we examine the concerns and challenges of IoT techniques, alternative IoT topologies, connectivity, middleware, and data processing technologies. Furthermore, the applications of IoT realm of agriculture are studied.
A 3D Metasurface Innovative Concept of Patch Antenna Gain Improvement for 5G Applications

In this work, a 23-25 GHz 3D Frequency Selective Surface (FSS) patch antenna dedicated to 5G applications was designed for gain enhancement purpose by incorporating a 4 × 5 unit cell FSS network. The overall dimensions of the resulting antenna are (40 × 40 × 34) mm3. A comparative analysis was conducted to evaluate its performance at various distances between the antenna and the periodic structure. Simulation results indicate a gain improvement of almost 7 dBi.
Language AI in Programming: A Case Study of ChatGPT in Higher Eduation Using Natural Language Processing

ChatGPT is an emerging technology used in education. It provides promising support to enhance teaching and learning activities. However, there is lacking understanding of its use to programming courses in computing programs. This study investigates the benefits, issues, and challenges of using ChatGPT, a language AI model, in programming tasks. Data was collected through surveys administered to undergraduate students in computing programs across various National Capital Region (NCR) universities. The collected data was analyzed using Natural Language Processing (NLP) and Latent Dirichlet Allocation (LDA) techniques to gain insights from the students responses. A word intrusion test assessed theme coherence, and evaluators demonstrated moderate agreement (Fleiss Kappa score: 0.62). The study reveals the benefits of employing ChatGPT for programming, including efficient coding, understanding complex codes, and its capability to be used as a problem-solving tool. However, it highlights critical issues and challenges, such as data privacy and ethical concerns, plagiarism tendencies, and contextual understanding limitations. The findings contribute valuable insights for developers, educators, and researchers interested in leveraging language AI models like ChatGPT to enhance programming workflows while effectively addressing associated challenges.
Modelling, Simulation and Performance Analysis of K-Thermocouples using PSPICE

Considering the lack of precise thermocouple models in the day-to-day circuit simulations, in the current work, the authors propose a Personal Simulation Program with Integrated Circuit Emphasis (PSPICE) model for a K-type thermocouple made with Analog Behavioral Modelling (ABM) blocks. The paper presents the steps to be taken to obtain the model, so that the user can create, in addition to the model already created and presented in this paper, his own PSPICE model for other types of thermocouples, each with its own characteristic.
Raw Coffee Bean Classification for Roasting Suitability Assessment Using Transfer Learning

The classification of raw robusta coffee beans is a pivotal process with profound implications for the coffee industry. Recognizing the critical significance of this classification, this research endeavors to establish an effective and automated method for discerning coffee bean quality. The primary objective of this study is to employ advanced machine learning techniques to classify raw robusta coffee beans accurately. Specifically, we utilize deep learning models, VGG-16 and Inception V3, for this purpose. These models are trained to classify coffee beans based on their quality attributes, offering a systematic approach to segregating beans into high and low-quality categories. The research methodology encompasses comprehensive data preprocessing, model construction, and rigorous experimentation. Notably, the Inception V3 model, initialized with pre-trained weights from ImageNet, emerged as the top performer, achieving an outstanding accuracy rate of 99%. The implications of this research extend to various stakeholders within the coffee industry. By enhancing the quality assessment of raw robusta coffee beans, this research contributes to improved quality control processes. It ensures that only the finest beans proceed through the production chain, bolstering economic viability and market competitiveness for coffee-producing regions.
Analysis of Face Recognition Technique: Plastic Surgery Altered Face

Human face recognition is used as an efficient tool in criminal identification but it deals with several challenges. One of the challenges that face by the face recognition system is plastic surgery. Plastic surgery is a sub-specialty of general surgery that focuses on correcting, repairing, or recreating defects or abnormalities in the human body, most commonly through the use of surgical techniques. Plastic surgery involves a wide range of subspecialties, such as breast augmentation, body contouring, and reconstructive surgery; however, it may also comprise treatments that are performed on the face. Enhancing face characteristics, fixing flaws, or enhancing attractiveness are the goals of facial plastic surgery. Recently their abrupt change in the field of plastic surgery it become easier to the criminal to pose a new face. In literature several face recognition challenges are addressed but problems in recognition of surgical altered face are not addressed thoroughly. In this paper, I will try to explore all the challenges of plastic surgery on human face recognition systems.