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In recent years, experimental building fire accidents occurred frequently in colleges and universities, which caused great economic losses and casualties. In this paper, based on the analysis of the causes of fire accidents, in order to understand the fire development process and the law of smoke spread in the experimental building, Pyrosim is applied to simulate the fire caused by inadvertent use of electrical appliances, and the smoke spread, visibility, temperature, and temperature during the fire development process are studied. The results show that in the process of fire development, both temperature and carbon monoxide concentration exceed the range of human body, and the safety of personnel evacuation does not meet the current national standards. Finally, according to the simulation results, the corresponding conclusions and suggestions for improvement are given in order to provide reference for the fire of experimental buildings in colleges and universities and improve the safety performance.

The development of the Internet and mobile technology has brought various changes to society. In particular, the growth of video platforms such as YouTube has allowed those who have watched videos through legacy media to enjoy videos freely at the time and place they want. The freedom of time and space, consequently, has changed content use behavior, causing a paradigm shift in media consumption. It has brought unprecedented changes in media consumption patterns such as vertical media, and short-form content. Starting with the new social and cultural changes brought about by the YouTube platform, this paper aims to examine the changes in newly emerged MCN companies and the media industry. In particular, this paper shall examine in depth the implementation of novel revenue diversification strategies by MCN companies, who are aware the limitations of advertising revenue received from YouTube. Such revenue diversification strategies of MCN companies appear to be excellent examples to understand and analyze trends in management strategies, as well as new marketing strategies in the digital age. By examining the changed media industry’s latest corporate management strategies, it is possible to derive two implications: management insight and sociological insight.

This platform is a platform that broadcasts the chef's cooking scenes live and teaches individual subscribers personalized cooking through auctions.The platform delivers the chef's hands-on cooking demonstration to customers in real time, and the auction-winning customers get the opportunity to participate in exclusive live broadcasts with the chef.This provides customers with an immersive learning experience, providing them with an opportunity to enhance not only culinary knowledge but also in-depth understanding and practical cooking skills.This platform places a lot of weight on social contributions beyond just commercial purposes The dishes created by the chef through live broadcasts are delivered directly to the socially underprivileged, especially the vulnerable in need of help, in collaboration with donor organizations. This demonstrates that cooking can be a means of embodying social values, not just commercial activities. In this way of operation, we want to realize a culture of sharing through cooking and combine the platform's existence value with social responsibility. Additionally, the platform provides customers with a variety of sales methods, with some popular content produced as meal kits based on clicks, subscriber reactions, and evaluation by restaurant experts. These meal kits are provided on a regular basis through the subscription system or sold in a way that the general consumer can also purchase individually. Some of the profits from meal kit sales lead to donations again, allowing the platform to have a virtuous cycle structure that continues to create social value. In conclusion, the platform redefines the modern culinary experience through a model that combines advanced culinary education with social sharing. It is creating a sustainable ecosystem that provides subscribers with special cooking experiences and in-depth academic opportunities, and at the same time provides practical help to the socially underprivileged through donations and sharing. Closely combined with culinary education, interaction, and social responsibility, the platform contains innovative attempts to incorporate the educational and social values of cooking to shed light on its new meaning and value.

We investigate the discourse on Twitter among overseas Koreans regarding voting intentions during the COVID-19 pandemic. Employing Snscrape 0.3.4 for data collection, we gathered tweets using a set of predefined keywords related to voting, COVID-19, and overseas Korean experiences. Our content analysis, grounded in both quantitative and qualitative methodologies, followed a rigorous coding scheme developed iteratively to capture the essence of the discourse, focusing on attitudes, subjective norms, and perceived barriers to voting during the pandemic. We found a significant shift in discourse, from initial information sharing and voting encouragement to a focus on the obstacles posed by COVID-19, including the closure of diplomatic missions and the impact of social distancing measures. The findings reveal a strong collective self-efficacy among overseas Koreans, who actively sought and shared voting-related information, encouraged participation, and proposed alternative voting methods. Theoretical implications extend to the realms of self-efficacy and the theory of planned behavior, illustrating how digital platforms can mediate political mobilization and participation in unprecedented circumstances. This study contributes to the understanding of global citizenship and political engagement in the 21st century, emphasizing the importance of structural support and digital platforms in facilitating the exercise of citizenship rights during global crises.

This paper introduces a decision-making framework for offloading tasks in home network environments, utilizing Distributed Reinforcement Learning (DRL). The proposed scheme optimizes energy efficiency while maintaining system reliability within a lightweight edge computing setup. Effective resource management has become crucial with the increasing prevalence of intelligent devices. Conventional methods, including on-device processing and offloading to edge or cloud systems, need help to balance energy conservation, response time, and dependability. To tackle these issues, we propose a DRL-based scheme that allows flexible and enhanced decision-making regarding offloading. Simulation results demonstrate that the proposed method outperforms the baseline approaches in reducing energy consumption and latency while maintaining a higher success rate. These findings highlight the potential of the proposed scheme for efficient resource management in home networks and broader IoT environments.

This study develops design principles for creating phygital (physical and digital) cultural heritage experiences, integrating advanced technologies such as VR/AR, digital twins, and interactive storytelling. Through thematic analysis of existing literature and validation via a professional survey, five key principles were identified: Human-Centered Design, Technological Integration, Narrative Fidelity, Cultural Sensitivity, and Sustainability. These principles offer a framework for preserving cultural authenticity while enhancing user engagement and accessibility. This study explores key challenges in integrating sustainability and cultural authenticity into phygital cultural heritage projects and provides cultural heritage professionals with flexible design strategies that leverage digital technologies to create immersive, educational, and culturally respectful experiences. These adaptable strategies ensure that projects remain viable, relevant, and capable of balancing innovation with preserving heritage integrity.

With the advancement of modern AI technology, the field of computer vision has made significant progress. This study introduces a parking management system that leverages Optical Character Recognition (OCR) and speech recognition technologies. When a vehicle enters the parking lot, the system recognizes the vehicle’ s license plate using OCR, while the administrator can issue simple voice commands to control the gate. OCR is a technology that digitizes characters by recognizing handwritten or image-based text through image scanning, enabling computers to process the text. The voice commands issued by the user are recognized using a machine learning model that analyzes spectrograms of voice signals. This allows the system to manage vehicle entry and exit records via voice commands, and automatically calculate paid services such as parking fees based on license plate recognition. The system identifies the text areas from images using a bounding box, converting them into digital characters to distinguish license plates. Additionally, the microphone collects the user's voice data, converting it into a spectrogram, which is used as input for a machine learning model to process 2D voice signal data. Based on the model’ s inference, the system controls the gate, either opening or closing it, while recording the time in real-time. This study introduces a parking management system that integrates OCR and a speech command recognition model. By training the model with multiple users' data, we aim to enhance its accuracy and offer a practical solution for parking management.

There are various sensor technologies used to obtain target information, such as camera-based position estimation methods, LiDAR, radar, and sensor fusion. Radar technology is capable of estimating long-distance targets and determining positions even in challenging environments, such as rain, snow, fog, and darkness. Sensor data provides position information such as speed, distance, azimuth, and elevation. This paper focuses on distance measurement among these position parameters. The method for acquiring distance information applies the linear limited minimum variance method to improve the signal-to-noise ratio of the received signal, remove interference, and estimate the distance from the radar to the target using the radar equation. Through simulation experiments, the transmission signal is generated by mixing the source signal and the interference signal, and the reception signal is input to the antenna. The target distance is estimated by removing signals other than the desired components from the received signal. The simulation results show that the signal-tonoise ratio is improved by removing the interference signal, and the target distance estimation accuracy is improved.

The primary objective of this study is to collect, clean, and analyze big data centered around news articles from portal sites and social media pertaining to League of Legends(LoL), a representative game in the esports industry. By extracting valuable information, semantic connections, and context from this unstructured data, we aim to provide practical implications for the esports industry. In order to collect popularity data of the most 'League of Legends' game among e-sports games, Textom, a big data solution service, was used to collect related keywords from October 8, 2023 to June 30, 2024. Textom collected data for Naver and Google. Specifically, 2,024 news sections, 8,874 blog sections, and 2,969 cafe sections were collected on the Naver channel. On the Google channel, 3,734 news sections and 59 Facebook sections were collected. Amounting to 17,660 materials. The collected data was analyzed using Textom and Ucinet 6.0. We conducted TF analysis and TF-IDF analysis through text mining, followed by matrix analysis and semantic network analysis. Additionally, CONCOR analysis was used to derive clusters of keywords with similar meanings. Based on the analysis results, the following conclusions were drawn. First, the most frequent keywords in the collected data were ‘LOL’, ‘game’, ‘Riot Games Inc.’, ‘sale’, and ‘skin’. The TF-IDF ranking was ‘game’, ‘Riot Games Inc.’, ‘sale’, ‘skin’, and ‘T1’. These two analysis results suggest that there is a high level of interest and issues related to purchasing LOL games and the developer. Second, through semantic network analysis, we identified three types of centrality. Considering the overall centrality, keywords related to competitions, developers, the T1 team, and time or seasons showed high centrality. Third, CONCOR analysis resulted in four clusters. First, as the main topic of this study is LOL, Cluster A consisted of keywords related to ‘e-Sports Game’. This cluster included the most influential and popular player, Faker, and tournament names such as the World Championship. Cluster B was the ‘LOL’ cluster, which is the main topic of the study. Keywords related to actual participation, such as game companies, skins, patches, and play, were central to this cluster. Cluster C centered around keywords related to ‘Strategy’ for winning games, such as ‘item build’, ‘Howling Abyss’, ‘strategy’, ‘Rune’, ‘item’, and ‘Counter’. Cluster D focused on keywords related to ‘Transaction’, such as ‘sale’, ‘price’, ‘deal’, ‘completion’, ‘private transaction’, ‘Ahri’, and ‘direct payment’.

We explore the latest trends and future directions in network security system development, with a focus on emerging technologies aimed at strengthening defenses against increasing cyber threats. Our study reviews recent advancements across critical areas such as encryption, intrusion detection, and secure communication protocols. Additionally, we examine the potential challenges and practical applications of these technologies, especially in the context of satellite networks. Through this research, we provide new insights into how these technologies might evolve to address future security needs, contributing a unique perspective on the practical deployment of these security measures.

In this paper, I study the Raft leader election process to enhance fault tolerance in a network composed of minimal nodes by considering various failure situations that may occur during consensus in a private blockchain network. In the process of processing network partition situations, node failure situations, and leader node failure situations, an Activity Score variable is set, so that the platform is configured with the minimum number of nodes in a network partition situation or node failure situation, and when successful leader election is required, it can be modified. Leader election is conducted according to the Raft algorithm, and leader node election and network failures are minimized based on trust to enhance fault tolerance even in a platform environment where the minimum number of nodes is operated. Excellent performance of over 12% on average was confirmed.

Interactive media art is a contemporary art form that fundamentally relies on the active participation and interaction of visitors to reach its full potential. This art form is intricately connected to the ongoing advancements in touchscreen technology, which serve as the primary interface through which audiences engage with the artwork. Unlike traditional static art forms, interactive media art transforms visitors into active participants, whose interactions are essential for completing and continuously evolving the artistic experience. Artists and groups such as TeamLab and Miguel Chevalier are at the forefront of this innovative approach, using touchscreen technology to create immersive, dynamic environments. In these installations, visitors engage with the art through touch, gesture, and movement, which in turn influences and transforms the artwork in real time.The combination of touchscreen technology and artistic expression will redefine the boundaries of creativity. As artists continue to embrace these technological advances, audiences will experience innovative and innovative art that invites them to participate in new ways.

In this paper, I study the application of blockchain technology in environments that require accurate handling of large-scale data, such as artificial intelligence, to enhance prediction accuracy and data performance. To address data privacy concerns and to strengthen trust in Data Privacy and security, I have researched the application-based performance of zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge) using formulated approaches. For performance evaluation, I designed and developed a smart contract based on the proposed content to ensure the implementation of zk-SNARKs. The results indicate that when compared to traditional pseudonymization algorithms like Pseudonymization and tokenization, zk-SNARKs improve confidentiality by 5-10%, data privacy by over 10%, and security by more than 20%.

We explore the effectiveness of AI-driven markerless motion capture (MoCap) tools compared to the traditional marker-based OptiTrack system, known for its high accuracy in capturing precise movements. Through detailed comparative analysis, we assessed various free markerless MoCap tools, including Move One, Radical, Deep Motion, Plask, Rokoko, and Movmi, focusing on critical aspects such as pose accuracy, movement smoothness, and ground detection. Our findings indicate that Move One is the most versatile tool, offering excellent pose accuracy, smooth MoCap, and reliable ground detection, making it a strong contender for various animation tasks. We found that Radical excels in minimizing jitter, making it suitable for projects requiring smooth motion, while Deep Motion performs best in ground detection, which is crucial for accurate foot placement. Although markerless systems still do not fully match the precision of marker-based systems, we suggest that they present viable alternatives depending on the specific needs of a project. As AI technology continues to advance, we expect the gap between markerless and marker-based to narrow, expanding the potential applications of markerless MoCap in the industry.

In strawberry cultivation, maturity classification plays an important role in ensuring the efficiency and quality of harvesting. In this study, we propose an Improved Faster R-CNN model to address these challenges, using MobileNetV3-Large as the backbone network to achieve a lightweight model, and introducing RoI Align to improve the spatial accuracy of the feature map. Experiments are conducted using the KGCV_Strawberry dataset, with precision, recall, F1 score, and mean average precision (mAP) measured for performance evaluation. The experimental results show that the proposed model achieves an average precision of 71.35%, recall of 71.07%, and F1 score of 71.21% across all classes. In particular, the proposed model achieves 63% performance on mAP0.5 and 58% performance on mAP0.5:0.95, which is comparable to existing ResNet-based models while achieving faster inference speed. The proposed model achieves a processing speed of 27.6543 ms, which is about 2 ms faster than existing ResNet-based models. This indicates that the goal of creating a lightweight model with improved image processing capability was achieved with minimal performance degradation. This research is expected to contribute to the development of automated strawberry cultivation systems in greenhouse environments and has the potential to be applied to various agricultural environments in the future.

One of the biggest drawbacks of online education using virtual environments is that teachers cannot see students' facial expressions. In offline classes, teachers usually observe students' expressions to determine if they are focused or enjoying the lesson, and they can adjust their teaching accordingly. For example, if a teacher notices that students are losing focus, they can slow down the pace of the lesson or tell an interesting story to regain their attention. However, in a virtual environment, it is impossible to see students' expressions, making it difficult to gather any information about them. As a result, instructors may feel like they are teaching in isolation and are unable to appropriately respond to students' reactions. This can easily lead to a lack of interaction between the teacher and students. This issue has already been raised in other studies, and research has been conducted to measure student engagement and attention. However, existing systems typically measure overall engagement for the entire class or represent the data in numbers or graphs, which doesn't provide impactful real-time feedback to the instructor. This study proposes an online education system that visually displays each student's level of engagement and attention in real time to address this issue. The key advantage of this system is that it allows teachers to quickly and intuitively grasp students' reactions and adjust their teaching in real time accordingly.

This study explores the significance of AI-driven smart cities and their geopolitical implications, focusing on efficient smart economies, social and environmental connectivity, and sustainability. France is leading in AI development and adoption within Europe, possessing the necessary infrastructure and technology for smart city implementation, yet it must address social inequality and the digital divide. Japan is leveraging AI for smart city development to tackle its aging population, excelling in technology innovation and infrastructure, but it faces challenges in social acceptance and data privacy. Ireland, as a European hub for major IT companies, is well-positioned for AI smart city construction, though it must overcome issues like housing shortages and infrastructure expansion. The Netherlands must address social conflicts and housing shortages caused by high population density and increasing immigration. For successful AI smart city development, it is crucial to integrate immigrants, expand housing, and create economic opportunities. South Korea's major platforms like Naver and Kakao, are poised to play a central role in the AI smart city era, leveraging their vast data analytics capabilities, robust telecommunications infrastructure, and strong user base to enhance global competitiveness.

This research develops a smart livestock monitoring system leveraging artificial intelligence with YOLOv8 and OC-SORT technologies to precisely monitor and analyze cow behavior, enhancing detection and tracking capabilities in complex environments. It delves into cows' movement speed and acceleration to uncover behavior patterns and health status, focusing on estrus-related behaviors for optimal breeding strategies. The study identifies changes in activity, social interactions, and mating behaviors as crucial estrus indicators, contributing significantly to livestock management innovations. By offering methods for visual behavior analysis representation, it simplifies the interpretation of findings, advancing livestock monitoring technology. This work not only contributes to smarter livestock management by providing an AI-driven cow behavior tracking model but also opens new avenues for research and efficiency improvements in the field.

This paper proposes a solution for innovating crime prevention and real-time response through the development of the Smart Drone Police System. The system integrates big data, artificial intelligence (AI), the Internet of Things (IoT), and autonomous drone driving technologies [2][5]. It stores and analyzes crime statistics from the Statistics Office and the Public Prosecutor's Office, as well as real-time data collected by drones, including location, video, and audio, in a cloud-based database [6][7]. By predicting high-risk areas and peak times for crimes, drones autonomously patrol these identified zones using a self-driving algorithm [5][8]. Equipped with video and voice recognition technologies, the drones detect dangerous situations in real-time and recognize threats using deep learning-based analysis, sending immediate alerts to the police control center [3][9]. When necessary, drones form an ad-hoc network to coordinate efforts in tracking suspects and blocking escape routes, providing crucial support for police dispatch and arrest operations [2][11]. To ensure sustained operation, solar and wireless charging technologies were introduced, enabling prolonged patrols that reduce operational costs while maintaining continuous surveillance and crime prevention [8][10]. Research confirms that the Smart Drone Police System is significantly more cost-effective than CCTV or patrol car-based systems, showing a 40% improvement in real-time response speed and a 25% increase in crime prevention effectiveness over traditional CCTV setups [1][2][14]. This system addresses police staffing shortages and contributes to building safer urban environments by enhancing response times and crime prevention capabilities [4].

Recently, with the rapid advancement of high-speed internet and interactive streaming technology, numerous video conferencing platforms such as Zoom, ZED, and Google Meet have been developed.. Furthermore, the COVID-19 pandemic, in particular, served as a catalyst for the global spread of online meetings and online education. This led to a significant reduction in people's resistance to online education and meetings. Nowadays, it has become common for people to use video conferencing platforms like Zoom not only for meetings but also for online education, seminars, and classes. Along with this trend, more specialized online platforms have been released, and research has been conducted on platforms that people can use more comfortably and for longer periods. This study specifically analyzes the current online video conferencing platforms from the perspective of Cognitive Overload and proposes a method to reduce Cognitive Overload. Additionally, a system was developed to address this issue. In other words, this research aims to analyze the limitations of previous online video conferencing and education platforms regarding Cognitive Overload and identity recognition, and to propose an online video conferencing and education platform that can overcome these challenges.

Wall-climbing robots have been safer alternatives to humans in hazardous industrial tasks. Propeller-based wall-climbing robots have gained attention because of their ability to travel on a wall surface with an arbitrary angle. In this study, the mechanical structure and thrust analysis of the robot is introduced, considering lightweight, efficient movement, and driving stability based on conventional propeller-driven wall-climbing robots. Additionally, the thrust analysis of the propeller was conducted through Computational Fluid Dynamics (CFD) simulation to enhance operational efficiency. This analysis shows that the height of the propeller from a contacting wall surface is a significant design parameter for the thrust. Furthermore, a 3Dprinted prototype robot based on the described contents is manufactured. This research is expected to provide insights for the structural design of propeller-based wall-climbing robots.

This study explores the integration of Generative AI into Test-Driven Development (TDD) to efficiently produce code that accurately reflects programmers' requirements in software engineering. Using the Account class as an example, we analyzed the code generation capabilities of leading Generative AI models—OpenAI's ChatGPT, GitHub's Copilot, and Google's Gemini. Our findings indicate that while Generative AI can automatically generate code, it often fails to capture programmers' intent, potentially leading to functional errors or security vulnerabilities. By applying TDD principles and providing detailed test cases to the Generative AI, we demonstrated that the generated code more closely aligns with the programmer's intentions and successfully passes specified tests. This approach reduces the need for manual code reviews and enhances development efficiency. We propose a development process that combines TDD with Generative AI, leveraging the strengths of both to efficiently produce high-quality software. Future research will focus on extending this approach to more complex systems and exploring automatic test case generation techniques.

The exponential increase in publications and the interconnected nature of sub-domains make traditional methods of information extraction and organization inadequate. This inefficiency can impede scientific progress and innovation. To address these challenges, this research leverages the ability of Bidirectional Encoder Representations from Transformers for keyword extraction (KeyBERT) and integrates with K-Means clustering to organize topics from large datasets effectively. Analyzing a dataset of 47,627 articles from SCOPUS in the domains of Reinforcement Learning and Computer Vision. An ablation study demonstrates the generalizability of the approach across these fields, with the optimal number of clusters determined to be three using the Elbow Method. The results demonstrate that KeyBERT is effective in extracting and organizing topics within these domains, with a particular focus on applications such as medical imaging, autonomous driving, and real-time detection systems. This methodology offers a scalable solution for organizing vast academic datasets, enabling researchers to extract meaningful insights efficiently and apply this approach to other domains.

This paper presents a novel deep learning-based radio frequency identification (RFID) tag collision detection method for ultra-high frequency (UHF) RFID. UHF RFID technology provides longer communication range compared to NFC, barcode, and QR code technology. However, due to the longer range, multiple tags in wide range may reply simultaneously such that a reader receives superposed signal of multiple tags. Multiple tag signals interfere with each other such that reader’s tag reading speed is decreased. In order to detect these tag collisions, previous studies utilized analytical methods rather than theoretical ones. Hence, a deep learning-based solution can improve the detection performance. For deep learning, training datasets are generated from mathematical equations, which are specified by the standard, with various delay times, amplitude differences, phase differences and noise level among tag signals. Arbitrary delay time, phase difference, and amplitude difference are used in every run of simulation. Simulation results show that the detection performance using the proposed method is about 5 dB better than that of existing method.

In this study, we analyze the transformation of webtoon storytelling patterns following the implementation of the ‘Free if You Wait’ monetization system in the industry. Initially offered without cost, webtoons underwent substantial industrial and creative shifts with Lezhin Comics’ introduction of paid services in 2013 and KakaoPage’s establishment of the ‘Free if You Wait’ model. Our objective was to explore how monetization has influenced storytelling techniques in webtoons. Specifically, we conducted a comparative analysis of Solo Leveling and Tower of God, examining how the payment model influenced narrative structures, episode pacing, and plot progression. Through this analysis, we conclude that Solo Leveling, optimized for the ‘Free if You Wait’ model, employs fast-paced storytelling, frequent cliffhangers, and substantial episode content that fosters reader immersion and incentivizes paid engagement. In contrast, Tower of God emphasizes prolonged narrative arcs and deep character relationships, maintaining a relatively slower pace.

Measures to enhance a cloud-based smart farm management system were proposed to improve its efficiency and maintenance. The existing system had achieved efficiency and stability by utilizing a web-based operating program with a general-purpose microcomputer and Linux. However, this system faced issues with synchronization and maintenance while concurrent tasks were being performed. Synchronization issues were solved by implementing an embedded DB, and the system was upgraded to allow over-the-air (OTA) software updates. Additionally, a method was also proposed to enable remote maintenance using tunneling. It was determined that applying the proposed method can contribute to the widespread adoption of smart farms, in addition to reducing maintenance costs. Furthermore, this system can also be expanded into a universal system applicable to different service models in the future.

We developed and validated a portable tablet-based system to assess brain motor control abilities by engaging participants in a manual tracking task with both visible and invisible targets, thereby eliciting feedback and feedforward control mechanisms. We measured the accuracy of these mechanisms using error terms, comparing 1) the performance of the dominant and non-dominant hands and 2) the intervals of feedback and feedforward control. We showed that the dominant hand demonstrated greater accuracy than the nondominant hand, particularly when tracking a faster-moving visible target. Furthermore, the non-dominant hand transitioned from feedback to feedforward control at a slower target speed compared to the dominant hand. This suggests differential motor control processing between hands. We present this tablet-based system as an accessible and versatile tool for assessing feedback and feedforward control during target tracking tasks, based on feedback-error learning theory. It enables efficient analysis of motor development in children, motor decline in older adults, and stroke rehabilitation outcomes from a brain motor control perspective.

In this paper, a Quadrature Voltage Controlled Oscillator of a wireless transceiver operating in the 5GHz UNII band of the wireless LAN 802.11a standard was proposed. In addition, a new structure of low-noise, low-power Quadrature Coupled VCO was proposed using the quadrature phase output as an input to the switching current source. If this structure is applied to other circuits such as a structure in which the current source is separated or a common use of a current source, a phase noise characteristic of 17 dB better than the existing VCO can be obtained. In particular, it is designed to operate with low power in a simple structure compared to the existing in-phase QVCO. The circuit was designed to operate with a supply voltage of 1.2V by the TSMC 0.13μm RF CMOS process. The measured VCO has a large tuning range of 20% operating at frequencies of 4.5 - 5.6 GHz, and phase noise of -117 dBc/Hz or less was obtained at the 1 MHz offset. The output phase error of the proposed QVCO was less than 0.5 degrees, and the total power consumption was able to obtain 5.3 mW at 1.2V.

Infrared-based scanners are utilized as a promising method for detecting objects that contact on a surface. In this system, infrared transmitters and receivers are positioned at opposite ends of the plane, facing each other. Traditionally, this system employed a one-to-one scanning method, where a single infrared transmitter emits a light signal that is detected by a corresponding receiver on the opposite side. While this method offers advantages such as fast response times and system simplicity, it is limited by its inability to detect multiple objects simultaneously. To address this limitation, recent applications have adopted the one-to-many scanning. In this scanning method, a single infrared transmitter emits a light signal that is detected by multiple receivers on the opposite side. The results are then read in real-time to determine the position and size of the object. With the recent advancements in computing power, the response speed and accuracy of one-to-many scanning have significantly improved. However, in most cases, this method has been limited to object detection on simple planes, and there is no analytical method available to support performance prediction when considering various sensor installation configurations with various form-factors. In this study, we mathematically modeled an infrared sensor array system to predict the performance of various sensor configurations installed on twodimensional planes or curved surfaces. Additionally, we assess the critical effect of inevitable positional errors (including orientation mismatches) on the system's performance. The unique approach introduced in this paper will provide highly reliable quantitative predictions, aiding in the design of sensor network form factors tailored for various applications in the future.

Semiconductors are crucial components in communication technology, playing important roles in various communication systems. They are essential for signal processing, data transmission, and ensuring the stability of communication networks. In particular, high-performance semiconductor chipsets and processors enable ultra-fast data transmission and ultra-low latency in communication technology. For example, semiconductors are indispensable in smartphones, wireless networks, and satellite communication systems. For semiconductor packaging products, nondestructive internal analysis for defect analysis and process improvement without causing deformation of system packaging is an important part of the product development process. In this study, nondestructive analysis techniques using X-ray equipment are discussed. The results of this study can provide fast and accurate nondestructive analysis of semiconductor packaging products and can play a significant role in supporting the growth of the communication industry.