Collaborative Optimization of Multi Modal Sensing Fusion and Visual Navigation

Pengyuan He, Jiachen Li, Yingjie Mao

pp.1-6 DOI: 10.62051/1gzs4354

Abstract: With the rapid advancement of intelligent systems such as autonomous vehicles and drones, multi-modal sensing fusion has emerged as a pivotal approach to enhance the robustness and accuracy of visual navigation systems. Traditional single-sensor solutions, including GNSS, IMU, and vision-based methods, face inherent limitations such as signal interference, error accumulation, and sensitivity to lighting conditions. This study proposes a collaborative optimization framework that integrates … Read More

Improvements on Solution-Based IGZO TFTs Optoelectronic Properties

Haoci Luo

pp.7-13 DOI: 10.62051/4pfsqw14

Abstract: Indium gallium zinc oxide (IGZO) gained significant attention in the semiconductor and display industries due to its high carrier mobility, excellent flexibility, and superior transparency. These features meet the growing demand for higher resolution and foldable displays. Solution-based IGZO thin-film transistors (TFTs) have demonstrated great potential for reducing production costs and enhancing the optoelectronic performance of display devices. However, several challenges remain, including … Read More

Uniformity Improvement Techniques for IGZO Thin Film Transistors

Yibo Zhu

pp.14-20 DOI: 10.62051/p6wzc795

Abstract: This study focuses on key technical strategies for enhancing the uniformity of indium gallium zinc oxide (IGZO) thin-film transistors (TFTs), a critical factor for their industrial scalability. A comprehensive comparison is made between conventional vacuum-based deposition methods, such as sputtering, atomic layer deposition (ALD), chemical vapor deposition (CVD), and emerging solution-based technologies, including spin coating, inkjet printing, and spray pyrolysis. The analysis identified … Read More

Doping Strategies for Promising Matel Halide Perovskite Based Solar Cells

Yuhan Feng

pp.21-25 DOI: 10.62051/3zbrgg15

Abstract: The metal halide perovskites have been broadly utilized as key materials in the development of solar cells due to their outstanding optoelectronic advantages. However, several challenges remain, including poor stability under high temperatures and humid conditions, the toxicity associated with lead at the B-site, and initial efficiency losses caused by ion migration. To address these issues, researchers have explored ion doping at various lattice sites. This paper first introduces the crystal … Read More

Research and Applications of EUV Lithography in Silicon Photonics

Heyi Zhang

pp.26-31 DOI: 10.62051/9j6m2m55

Abstract: Extreme Ultraviolet (EUV) lithography is a photolithography technology used mainly in semiconductor manufacturing, especially for advanced nodes at and below 5nm. This technology holds promise for future adoption in photonic integrated circuits like silicon photonics, due to its superior resolution, overlay accuracy, and higher throughput than the current Deep ultraviolet (DUV) lithography method. While the EUV lithography system theoretically can improve the performance of silicon photonic … Read More

Research and Application of Compute-in-Memory Architectures: RRAM, MRAM, and FeRAM

Yichang Chen

pp.32-38 DOI: 10.62051/e68jey36

Abstract: With the rise of data-intensive applications such as artificial intelligence, big data analytics, and edge computing, the performance constraints of traditional memory technologies have become noticeable. Next-generation non-volatile memory devices, including resistive random-access memory (RRAM), magneto resistive random-access memory (MRAM), and ferroelectric random-access memory (FeRAM), are being actively explored to address demands for higher speed, lower power consumption, and greater … Read More

Research on Photolithography Technology and Photoresist Materials in Chip Manufacturing

Rui Huang

pp.39-48 DOI: 10.62051/msyd5c27

Abstract: As semiconductor manufacturing technology evolves, lithography, a core process, faces the dual challenges of achieving higher resolution and smaller feature sizes. This paper explores the fundamental principles and development history of lithography technology, as well as its bottlenecks. It also delves into the differences between conventional organic and novel inorganic photoresists, with a particular emphasis on the unique advantages and reaction mechanisms of novel inorganic photoresists in … Read More

Research and Application of Genetic Algorithms in Semiconductor Devices

Hanwen Zhang

pp.49-56 DOI: 10.62051/x18s3t53

Abstract: Semiconductor technology underpins a vast array of modern electronic systems, yet continued device scaling and rising design complexity now confront fundamental physical and manufacturing limits. This thesis explores the use of genetic algorithms (GAs)—population-based, gradient-free optimizers inspired by natural selection—to address key challenges in semiconductor development. We first review GA methodologies, detailing their evolutionary operators and workflow steps. Next, we survey GA … Read More

Research Progress on Performance Improvement of Spin Valve

Jianchen Wu

pp.57-71 DOI: 10.62051/1f17dj71

Abstract: Spin valves, as core devices in spintronics, hold an irreplaceable position in cutting-edge fields such as information storage, magnetic sensing, and quantum computing. With increasing application demands, enhancing spin valve performance has become a common focus in both academic and industrial sectors. This paper systematically reviews recent research progress in spin valve performance enhancement, exploring key technological pathways for improving magnetoresistance ratio, enhancing … Read More

Financial Time Series Forecasting: A Hybrid Approach Combining AR-GARCH and Machine Learning Models

Shangrong Han

pp.72-77 DOI: 10.62051/pg9aec47

Abstract: Accurately forecasting financial markets remains a central challenge in economics due to the volatile, nonlinear, and complex nature of asset price movements. Traditional statistical models like Autoregressive Generalized Autoregressive Conditional Heteroskedasticity (AR-GARCH) have been widely used for modeling volatility, but often fall short when addressing intricate market patterns. This study systematically compares the predictive capabilities of AR-GARCH, Long Short-Term Memory (LSTM) … Read More

Evaluating Human-Like Qualities in Language Models

Ruijie Liu

pp.78-85 DOI: 10.62051/yygprz73

Abstract: This paper investigates the human-like communication abilities of modern language models, comparing several open-source and proprietary systems. As LLMs are increasingly deployed in socially interactive roles—ranging from digital companions to mental health support tools—their ability to engage users naturally and expressively has become a critical yet underexplored dimension of evaluation. Traditional benchmarks tend to emphasize accuracy or reasoning, but they fail to capture the nuanced, … Read More

Advancements in Natural Language Processing: A Study of Knowledge Graph Embedding Techniques

Jinsong Liu

pp.86-91 DOI: 10.62051/1tjsff20

Abstract: The use of Natural Language Processing (NLP) has made it possible for machines to understand, interpret, and produce human language, making it a cornerstone of artificial intelligence. The ability to represent and reason with structured knowledge is crucial for advancing NLP capabilities. Knowledge Graphs (KGs) offer a powerful way to model entities and their relationships, and learning low-dimensional vector representations of these components is the objective of this technique. This paper … Read More

A comparative study of Contrastive Self-Supervised Learning (CSSL): Methods, Technologies, and Applications

Zhenghan Li

pp.92-97 DOI: 10.62051/2hgr2j26

Abstract: With the continuous increase in the cost of data annotation and the explosion of diverse demands, traditional supervised learning is confronted with two major problems: the difficulty in annotating a large number of labels and the expansion bottleneck. Contrastive Self-Supervised Learning (CSSL) provides an effective solution for deep feature extraction in a label-free environment by constructing sample pairs and maximizing their discrimination in the feature space. This review takes Contrastive … Read More

The single inverter is affected by electromagnetic interference, and the protection mode

Zhenyu Zhang

pp.98-102 DOI: 10.62051/mas0f724

Abstract: The easiest way to resist electromagnetic interference is to optimize the design to reduce interference. The electromagnetic interference received by a single inverter mainly comes from the influence of electromagnetic radiation on circuit topology and device type. Regarding circuit topology, it is recommended to adopt soft switching topologies such as active clamping flyback or LLC resonance, which can effectively reduce the voltage and current change rate in the switching process and reduce … Read More

Research on a Chinese Text Information Density Evaluation Model Fusing Semantic and Statistical Features

Zhaoyang Ye

pp.103-114 DOI: 10.62051/ggfj0v47

Abstract: To address the issue in Chinese text information content evaluation where traditional methods primarily rely on statistical features and overlook semantic and structural complexity, this study proposes a Chinese text information density evaluation model that fuses semantic and statistical features. The model adopts a dual-channel fusion architecture: the semantic channel utilizes the pre-trained language model BERT to extract deep contextual embeddings of the text, combined with a Bidirectional … Read More

Research on Neighboring Right Protection Model of Artificial Intelligence Generated Content

Xiang Li

pp.115-121 DOI: 10.62051/4pk59j91

Abstract: Through interdisciplinary analysis, this paper analyzes the concepts and principles of AI-generated content from the perspective of computer science, so as to characterize AI-generated content and distinguish it from general human-created works. Through the historical analysis method, the author examine the historical process of AI and recognize the dilemmas faced under the protection of the current narrow copyright law, so as to try to find a new path of protection outside it. The rights … Read More

Analysis and Forecast of the Development of the Pet Industry in China and Globally Based on ARIMAX and Regression Models

Zhenjia Chang, Yuan Wei

pp.122-129 DOI: 10.62051/6xhmn512

Abstract: This paper systematically analyses the development trends of the pet industry in China and globally based on the ARIMAX model and various regression models. The study first collects data on the number of pets in China and influencing factors such as GDP and population. It then uses the ARIMAX (p,d,q) model combined with the AIC criterion to determine parameters, constructs a multi-factor regression model to analyse key influencing factors, and predicts trends. For the global market, data from … Read More

Multi-objective Integration and Optimization Research on Urban Waste Sorting and Transportation

Yihao Sun, Wenqing Jia, Yitong Wei

pp.130-137 DOI: 10.62051/thnzwf20

Abstract: This paper focuses on the challenges of urban waste sorting and transportation scheduling, establishing a mathematical modelling and optimisation framework that integrates vehicle path planning, multi-vehicle collaborative scheduling, and facility location optimisation. The study first establishes a CVRP model for single-vehicle route optimisation, employing an improved heuristic algorithm (combining PathCheapestArc and the 2-opt operator) to achieve efficient solutions. Next, in multi-vehicle … Read More

Multi-objective Prediction Model based on GA-BP Neural Network and Logistic Regression

Jingzhi Zhou, Yang Gao, Junchao Su

pp.138-151 DOI: 10.62051/asbnwr57

Abstract: This study develops a multi-objective prediction model integrating two complementary approaches to address complex prediction tasks in hierarchical data structures. The first is a hybrid Genetic Algorithm-Back Propagation Neural Network (GA-BP), which utilizes advanced feature selection techniques, including Lasso regression and XGBoost, to identify key predictors while addressing nonlinear dependencies and convergence issues. The GA-BP model achieves enhanced robustness, effectively modeling … Read More

Generative and Discriminative Models in Multimodal AI: An Analysis of Vision-Language Tasks

Fengjiang He

pp.152-160 DOI: 10.62051/hdjsgp39

Abstract: The transformer architecture has triggered groundbreaking works in multimodal vision and language (V+L). This article offers brief look into the two main modeling paradigms—generative and discriminative—from their roots in natural language processing (NLP) specifically generative pre-trained transformer (GPT) and bidirectional encoder representations from transformers (BERT), respectively. The core ideas of these two paradigms are then examined to show how they have been modified to handle V+L … Read More

A Review of Object Detection Empowering Sports: Key Technologies, Application Scenarios, and Future Outlook

Siyi Zhao

pp.161-170 DOI: 10.62051/fb5aqp52

Abstract: Object detection is now a cornerstone of 'Smart Sports,' yet the direct application of general-purpose models to the dynamic and often chaotic sports environment is fraught with challenges. This paper systematically reviews the core technologies of object detection in sports, including the adaptability and limitations of mainstream detectors (e.g., the YOLO series, Transformer-based models) in sports scenarios. It also examines the role of optimization strategies such as model pruning, … Read More

Research on Intelligent Operation and Maintenance Decision Support for Electrical Systems by Convergence of Artificial Intelligence and Edge Computing

Shaocheng Ma

pp.171-176 DOI: 10.62051/d98zh388

Abstract: Aiming at the problems of poor real-time and low decision-making efficiency that exist in the operation and maintenance of traditional electrical systems, this thesis conducts an in-depth study on the application of the fusion technology of artificial intelligence and edge computing in the decision support of intelligent operation and maintenance of electrical systems. By analyzing the synergistic mechanism of artificial intelligence algorithms and edge computing architecture, the fusion model … Read More

Application of Signal Processing and Pattern Recognition Theory in Fault Diagnosis of Electrical System

Yutao Guo

pp.177-184 DOI: 10.62051/81fmxb22

Abstract: The application of signal processing and pattern recognition theory in fault diagnosis of electrical system is discussed in this paper. Due to the continuous expansion of the scale and complexity of the power system, the traditional fault diagnosis methods have been unable to meet the modern needs. This study first introduces the common types and characteristics of electrical system faults, and then focuses on the analysis of the role of signal processing technology in fault signal extraction … Read More