Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentiment Analysis using DistilBERT

Transformers is an architecture that performs well in NLP task. To understand and improve its performance on sentiment analysis, DistilBERT is employed as the base model. Sentiment analysis is a process that extracts subjective information from textual data and categorizes them into different classes. The classification classes may include polarity (positive, neutral, negative) or emotions (happy, sad, angry). In addition, multiple techniques such as fine tuning, regularization and hyperparameter tuning are applied to improve the performance of the model. The proposed solution acquired an accuracy score of 85.41% on Internet Movie Database (IMDB) dataset and 86.59% on Customer Reviews (CR) dataset.
Capacity Enhancement Technique via CRAT in an MU-MIMO System with Statistical Power Analysis

This study aims to compare the effectiveness of the CRA technique and PRA technique in an MU-MIMO system and determine which technique can better enrich the total capacity of the system. Group 1 developed the cooperative resource allocation algorithm, while Group 2 created the priority resource allocation algorithm. The statistical power analysis was performed using a G power level of 80% and an alpha value of 0.061. The study is expected to provide insights into the effectiveness of CRA and PRA techniques in an MU-MIMO system and determine which technique can better enrich the total capacity of the system. The study will also provide recommendations on the optimal number of users and resources that should be used to achieve the best performance using CRA and PRA techniques. The novel CRA technique outperforms the PRA technique in terms of sum capacity performance for the MU-MIMO of the wireless network.
A Precomputed Atmosphere Differentiable Renderer for Estimating Outdoor Illumination

Estimating outdoor illumination from images requires modeling the complex phenomena of atmospheric scattering. Existing methods either depend on known scene geometry or multi-view images, limiting their applicability. This work presents a differentiable renderer tailored for atmospheric scattering to recover outdoor illumination from a single image. We first construct a physics-based atmospheric scattering model to establish the analytical mapping from the sun's zenith angle to sky appearances. We propose a pre-computation scheme to cache intermediate atmospheric transmittance, scattered radiance, and ground-reflected radiance for efficient gradient-based optimization. Their gradients of the sun's zenith angle can also be precomputed and stored in derivative tables. The desired gradients can be quickly retrieved at render time to guide the optimization. Experiments on sky image rendering and illumination estimation demonstrate that our method can recover plausible outdoor illumination from a single photograph. Without the need for scene geometry or multi-view inputs, the proposed differentiable rendering framework provides an efficient way to estimate outdoor illumination.
Generative Adversarial Networks (GANs) in Computer-Generated Imagery

In order to estimate the model using the method it is necessary to train two different models simultaneously. The first model, called the model (D) is responsible, for determining whether a particular sample is from the training data or generated by another model called G. On the hand G aims to understand the underlying distribution of the data. By training G to maximize the likelihood of D making mistakes a competitive environment is established. There are applications for G and D but one particularly notable solution emerges. D consistently evaluates any input as if it came from training data while G effectively captures the details of that training data distribution. Notably this system can be trained using known techniques like backpropagation when implementing G and D as multilayer perceptrons. This eliminates the need for inference networks or intricate Markov chains, during both training and sample generation processes. Thorough evaluations of the generated samples confirm the potential of this framework as demonstrated through testing. This innovative approach offers an efficient means to capture data distributions, which could potentially revolutionize generative modeling.
Machine Learning-Based Trustworthy Diabetes Prediction Model

Diabetes is a condition that develops when the blood sugar is above average level. Diabetes shoots up the risk of eye, kidney, nerve, and heart problems. By adopting suitable measures to prevent or manage blood sugar level may reduce the chances of getting diabetes-related health issues. Medical data collection is not done on a regular basis but is determined by the patient's condition and other clinical or administrative factors. A trustworthy model is free from missing values, faulty data and makes reliable predictions. Factors like data quality, anomalies, and model selection play a significant role in building a trustworthy model. Hence, this work concentrates on the prediction of diabetes by considering the following issues: 1) missing values in the dataset, 2) an imbalanced dataset and the presence of anomalies, and 3) diabetes prediction using Machine learning (ML) algorithm. The main objective of this work is to propose a trustworthy diabetes prediction model for an incomplete and imbalanced dataset using ML-based imputation, techniques for balancing datasets, and anomaly detection.
A Basic Iterative Non-Sign Average Gradient Adversarial Attack on Brain Tumor Image

As one of the major diseases that pose a threat to human health, brain tumors' diagnosis plays a vital role in the intervention and rehabilitation process. Classification models based on deep neural networks (DNNs) have been proven effective in aiding doctors to diagnose brain tumors through MRI images. However, the injection of tiny perturbations to the images can have a significant impact on the diagnosis, indicating the vulnerability of DNNs. Therefore, research on adversarial attack methods are highly significant in the medical diagnosis field. In this paper, we combined the non-sign gradient attack method and average gradient to propose a non-sign average gradient attack method(ABINM). By performing attack experiments on brain tumor MRI images under three different classification models, it was verified that the ABINM method can achieve higher rate of attack success and the generated adversarial sample shows stronger transferability on white-box and black-box testing compared to the baseline gradient-based adversarial attack methods.
Image Captioning for Chest X-Rays Using GRU - Based Attention Mechanism

Medical image captioning is a field that combines medical imaging with natural language processing (NLP). Since the development of deep learning techniques in both NLP and medical image analysis, this area of study has gained significant progress. This research aims to develop a system for providing a brief description when given an image of chest x-ray using a GRU-based Attention Model, which gave better training and validation accuracy and BLEU scores when compared to a simple encoder-decoder, attention mechanism implemented with context flow and global flow and other state-of-the-art models using chexnet. The Chest X-Ray Images dataset, consisting of a large number of images of different chest x-rays suffering from pneumonia with different points of view (anterior-posterior), is utilized for training and validation of the model. The proposed model is trained using a combination of chest x-ray images along with their text features. The findings demonstrate the potential of using the GRU-based attention model for detection and classification of pneumonia, which can be applied for diagnosis in hospitals, clinics, emergency departments and screening programs.
Fuzzy Based Approaches for Autism Spectrum Disorder Detection: A Review

Autism Spectrum Disorder is a neurodevelopment state manifest through difficulties with community interaction, constrained repetitive activity patterns and, the presence of specific interests or occupations. ASD is mainly caused by genetics or by environmental factors; however, its condition can be improved by treating and detecting ASD at its earlier stages. There are many ways to detect ASD, including psychological exams and neuro imaging modalities. Fuzzy logic is working in many sophisticated applications of artificial intelligence, including natural language processing. Fuzzy logic simulates human decision-making, but much more quickly. The main and the foremost objective of this paper is to represent an overview and provide the in-detail briefing of fuzzy logic methodology and concepts for detection of ASD. This paper discusses a systematic and literature review through a variety of scholarly and academic research papers, professional expert discussions and online databases. The goal of this study is to examine Fuzzy logic based expert systems for ASD identification.
Weather Image Recognition Using Vision Transformer

Weather significantly impacts human activities, and accurate weather recognition is crucial to mitigate the risks associated with severe weather conditions. In this research project, we propose Vision Transformer for weather image recognition. The goal is to identify weather patterns and conditions accurately to enhance safety in activities that are affected by the weather. To demonstrate the performance, additional five methods have been adopted to carry out the comparison, including K-Nearest Neighbors, Random Forest, Convolutional Neural Networks, Residual Network, and Compact Convolutional Transformer. Our experimental results show that the proposed Vision Transformer model achieved the highest accuracy of 99.58%, which outperformed the other models. This finding highlights the potential of deep learning techniques for accurate weather recognition.
A High-Performance Dual-Polarized Base Station Antenna Array for 5G Applications

This paper introduces a high-performance dual-polarization base station antenna array for 5G applications. The antenna array is a linear 1×4 array, which is composed of four cross-polarized dipole antenna units arranged in a line. $\pm \mathbf{45}^{\circ}$ dual polarization is achieved by cross-placing two dipoles. Microstrip baluns are used as the feeding structure for each dipole. By designing multiple metal via holes in the dielectric substrate of each dipole antenna to achieve high gain. Low sidelobe levels are achieved by feeding the antenna elements with unequal power distribution using a one-to-four power divider. The antenna array exhibits excellent performance within the operating range (3.3 GHz-3.7 GHz), with return loss less than -12 dB and isolation exceeding -20 dB. The proposed antenna array is not only structurally simple but also exhibits excellent performance, which has great potential for widespread applications in 5G.
Facial Emotion Recognition Method Based on Canny Edge Detection Using Convolutional Neural Network

Facial Emotion Recognition(FER) has made a pivotal contribution to Human Society by accomplishing complex tasks which humans struggle to achieve. The traditional Machine Learning (ML) models that follow explicit feature extraction from images require domain expertise, manual effort and also lack spatial invariance. The primary cause for the hindrance of the recognition accuracy is the imbalance of classes that exist in the datasets along with the limited availability of training data. To address these limitations, a facial emotion recognition method based on Canny edge detection utilizing a convolutional neural network is proposed in this paper. Important structural information from facial images is extracted to intensify the recognition rate, and techniques such as data augmentation, and oversampling are utilized. The proposed Convolutional Neural Network (CNN) model is trained using the dataset FER2013. The accuracy achieved with only 40 iterations of training was 76.92 percent with the devised method that surpasses other CNN architectural methods such as traditional CNN, Xception and AlexNet.
Comprehensive Averaged Model of DCM-Operated Boost Converter Fed by PV Source

The average model of DC-DC converters is commonly used to analyze the control stability and dynamics of switched-mode converters. Although the average model of the boost converter (BC) in the continuous conduction mode (CCM) and the discontinuous conduction modes (DCM) is well-covered in the literature, the modelling and control of the DCM-operated BC with the maximum power point tracking (MPPT) has not been paid much attention. In this paper, a comprehensive state-space averaged model of DCM-operated BC fed by photovoltaic (PV) source is derived. Higher-order, lower-order, reduced-order, and full-order models are considered. MATLAB simulation is used to verify the derived models. The converter dynamics were analyzed based on the bode-plot and the step-response for each model. Results show that the lower-full-order model is considered the most suitable choice for modelling the DCM-operated boost converter.
Systematic Review of Formation Control for Multiple Unmanned Aerial Vehicles

Multi-Unmanned Aerial Vehicle (UAV) formation control, enabling a group of UAVs to maintain a specified geometric pattern amidst environmental constraints, is an emerging research domain. This paper renders an exhaustive review of multi-UAV formation control methods. Initially, we delineate the primary challenges of formation control and set a foundational framework. This framework is segmented into three layers: formation control, communication topology, and motion control, each operating under potential external environmental constraints, with integral issues and strategies explored. Following, we categorize existing control strategies into five groups: leader-follower, virtual structure, behavior-based, consensus-based, and bio-inspired self-organizing methods. For each, we outline distinctive features, review relevant literature, and discern their strengths and weaknesses. In conclusion, we assess the current research state, anticipate future trends, and underscore the deficiencies and challenges of current strategies. This paper aspires to provide a thorough overview of formation control in multi-UAVs.
Geochemical Data Clustering Using UMAP: A Comparative Study on the Rapel River Fluvial System

In the field of geosciences research, due to the large amount of complex data, clustering methods are necessary as a first approach to provide insight into the data without previous analysis. Uniform Manifold Approximation and Projection (UMAP) has recently gained popularity as an effective clustering algorithm in several fields. We explore the application of UMAP as a pre-processing technique to enhance clustering results on 89 geochemical samples from the sediments of the Rapel River fluvial system with 25 variables. The clustering method was performed using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDSBSCAN), and the results were evaluated using the Silhouette Score. The generated clusters were then compared to the data origin results and interpretation. We demonstrate that UMAP can construct a favorable representation for clustering purposes, distinguishing different rivers denoting their own characteristics based on their geochemical data.
HBT Modeling and X-Band Differential LNA Design with 0.25 µm SiGe-C BiCMOS Process

This study presents the generation of a model based on measurement-aided numerical calculations for HBT (Heterojunction Bipolar Transistor) with VBIC parameter set. Various HBT structures with several different sizes are fabricated in an in-house developed SiGe BiCMOS process. The validity of the constructed model is verified with the measurement results. An RF Low Noise Amplifier is also implemented with HBT VBIC based model. This work explains all the steps of HBT transistor modeling. The VBIC model based HBT performance is validated in an implemented RF Low Noise Amplifier.
Enhancing Archeological Building Reconstruction Through Image-Based 3D Modeling and LoFTR

Archaeological research often relies on the meticulous reconstruction of historical structures and artifacts. However, this process is frequently hindered by the absence of detailed 3D data and the limited availability of well-preserved sites. In response to these challenges, this thesis tries to contribute in rendering the 3D structure of archaeological buildings from 2D unstructured image. In case of damage to the original buildings, its 3D model can be helpful for replication. The images from the internet are taken to train the model using Structure From Motion (SfM) based algorithms. A model named Detector-Free Local Feature Matching with Transformers (LoFTR) is utilized for local image feature matching. Ultimately, the feature points are adjusted to generate a 3D representation of the building. The key stages of structure from motion encompass feature detection and matching, outlier removal, camera pose estimation, point cloud creation, and 3D mesh generation. This reformulation method can reduce the expense and intricacy associated with traditional approaches.
Design of Radix-8 Based Approximate Booth Squarer for Error Tolerant Computing

To explore the benefits of approximate computing, this project proposes an approximate partial product generator for squarer (APPGS). Using APPGS, three designs of approximate radix-8 Booth squarer's (ABS1, ABS2, and ABS3) are proposed. APPGS produces approximate partial products in number of least significant columns of the partial product matrix. ABS2 and ABS3 utilize approximate adders and compressors with a novel input signal rearrangement method for the accumulation of approximate partial products. Moreover, the ABS3 features an error recovery module at k number of most significant columns of the approximate partial products. The proposed squarer's with different values of rand k are simulated and synthesized using 45-nm CMOS technology. The results indicate that the proposed squarer's achieve optimized performance for both hardware and accuracy metrics. The results indicate that the proposed radix-8 squarer's achieve optimized performance for both hardware and accuracy metrics as compared existing radix-4 squarer. After synthesis of the proposed design that helps to reducing area and power of the overall design.
Real-Time Collaborative Gaming using Multiagent Brain-Computer Interfaces

In this research explores the convergence of real- time collaborative gaming and multiagent brain-computer in- terfaces (BCIs), aiming to revolutionize interactive gaming ex- periences. The study investigates the feasibility and potential of employing BCIs to facilitate seamless and synchronized gameplay among multiple participants. The contemporary gaming landscape demands heightened immersion and interactivity. Traditional control interfaces often constrain the user experience. This paper addresses this challenge by proposing a novel approach that leverages multiagent BCIs to enhance real-time collaborative gaming engagement. A controlled experiment was conducted involving participants equipped with BCIs, interconnected to a gaming platform. Brain signals were recorded and analyzed to develop a multiagent framework for collaborative gameplay. The system allowed participants to influence game dynamics and communicate in real time through neural commands. The results demonstrate the successful integration of multiagent BCIs in collaborative gaming scenarios. Participants exhibited improved synchronization, leading to enhanced team coordination and gameplay immersion. This research paper aims to bridge the gap between real-time collaborative gaming and neural interfaces by introducing a multiagent BCI paradigm. By fostering enriched interaction and synchronized gameplay, this study contributes to the ongoing evolution of immersive gaming experiences, paving the way for future innovations at the intersection of gaming and neurotechnology.
Ocular Artefact Removal from Electroencephalogram Signals: A Review

Electroencephalogram (EEG) is widely utilized since it provides information on brain activity without causing any harm to the subject. EEG's great sensitivity makes it vulnerable to artefacts from both the environment and the brain. Blinks and eye movement, head movement, pulse, and so on may all generate artefacts in EEG recordings, which is the primary source of poor signal quality. Artefacts complicate the analysis of EEG data and may lead to erroneous conclusions about brain activity because they overlap with the signal collected from the brain. The creation of algorithms to detect and eliminate artefacts from EEG signals is crucial for obtaining such precise and trustworthy signal information. In this study, we take a look at the state of the art in removing artefacts caused by blinks and eye movements from EEG data. However, conventional artefact removal methods cannot eradicate all noise and will result in data loss due to the indiscriminate elimination of contaminated parts. The research demonstrates that combining conventional with other approaches is widely utilized because it may enhance artefact removal. The most up-to-date method for eliminating artefacts is an automated one that uses machine learning and deep neural networks.
Research on Low-carbon Transition Path of Urban Power System under Carbon Peak and Carbon Neutrality Targets : A case study of a large city in China

Under the premise of grasping the main line of low-carbon development, it is necessary to arrange a comprehensive carbon reduction path for the power system to ensure the steady transition of the urban power system under carbon neutrality target. This paper studies the low-carbon transition path of a large city power system in China, including the carbon emission target of the power industry and development of various power supply in the medium and long term. The results show that the city’ s renewable energy, nuclear power and clean power outside the region will become the important support for the city’ s new electricity supply and carbon emission reduction in the future.
GEOCHEMISTRY OF HULUHE OPHIOLITE AT EAST QILIAN OROGENIC BELT AND ITS TECTONIC SIGNIFICANCE

The Huluhe ophiolite is mainly composed of metoamorphic peridotite,olivine pyroxenite,layered and massive gabbro,pyroxenite diorite,tonalite and massive basalt,diabase and thin-bedded chert,chert limestone.its produced from Sinican Period-Early Paleozoic.This paper discusses the feature of geology,lithogeochemical of this ophiolitic belt,so the ophiolite is produced from near the back-arc basin spreading ridge.The Huluhe ophiolite zone includes "Early Paleozoic of Yemashan-Mouli-Lajishan suture zone" a eastward extension.
Truck-and-drone Collaborative Delivery Method Based on Adaptive Simulated Annealing Algorithm

The collaborative delivery of trucks and drones can comprehensively take advantage of the advantages of the two means of transportation. The emphasis of this paper lies in addressing the logistics distribution challenges associated with individual vehicles and drones. This paper established a two-level path planning model for truck and drone collaborative logistics distribution. Based on the characteristics of truck-and-drone collaborative logistics distribution problems, various specific neighborhood structures and an adaptive neighborhood selection mechanism have been meticulously designed, after that, an adaptive simulated annealing algorithm (ASA) was proposed. The results of simulation experiments demonstrate that the proposed method efficiently searches for satisfactory solutions for the collaborative delivery path between trucks and drones across various delivery ranges and quantities of packages in a relatively short timeframe. The cost of truck-and-drone collaborative delivery is reduced by approximately 50.25%, 14.13%, and 7% compared to the initial solution of truck separate delivery, vehicle machine collaborative delivery, and the optimal solution of the traditional simulated annealing algorithm, respectively. This method effectively reduces logistics delivery costs.