Computer Engineering Commons^™

เทคนิคการจัดกลุ่ม K-Means แบบการคำนวณควอนตัม, ภานุวัฒน์ ธนาภรณ์ชินพงษ์

Chulalongkorn University Theses and Dissertations (Chula ETD)

วิทยานิพนธ์ฉบับนี้ศึกษาอัลกอริธึม K-Means แบบผสมระหว่างควอนตัมและคลาสสิก สำหรับการจัดกลุ่มข้อมูลผู้ป่วยโรคหัวใจ โดยใช้วงจร swap-test ของควอนตัมในการคำนวณระยะทาง และได้ทำการทดสอบบนควอนตัมคอมพิวเตอร์จำลองใน 2 แนวทาง คือแบบที่มีสัญญาณรบกวน และแบบอุดมคติ ด้วยชุดข้อมูลจริงที่มีมากกว่า 1,000 รายการ ผลการทดลองแสดงให้เห็นว่า วิธีควอนตัมทั้งสองสามารถทำความแม่นยำได้สูงถึง 0.83 และให้ค่า F1-score ใกล้เคียงกับ K-Means แบบคลาสสิก (0.82–0.83) แม้ในกรณีค่าจากควอนตัมคอมพิวเตอร์ที่มีสัญญาณรบกวน ผลการศึกษานี้ชี้ให้เห็นถึงศักยภาพในการใช้งานจริงของวิธีจัดกลุ่มที่ได้รับการเสริมด้วยควอนตัม

Dynamic Modeling And Control Of A Solid State Semiconductor-Based Transformer, Microgrid And Storage Systems, Rubén Darío Viñán-Velasco

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Smart Grids are power grid models designed with the idea of including the growing new technologies, from generation to storage devices, and are a response to the growing demands from consumers and the presence of electronic components being commonplace in the modern devices. The design requires a dynamic alternative in order to build an independent grid that can also work in cooperation with other micro-grids and the power grid in an integrated way. Smart-grids present several advantages over the traditional power grid scheme, but the economic costs of the components required to implement smart-grids is currently a great limitation. This …

การพยากรณ์จำนวนสายโทรศัพท์เข้าของศูนย์บริการข้อมูลทางโทรศัพท์แบบรายครึ่งชั่วโมง, กฤตชญา ประภารัตน์

Chulalongkorn University Theses and Dissertations (Chula ETD)

การวิจัยนี้มีวัตถุประสงค์เพื่อศึกษาโมเดลที่เหมาะสมสำหรับการพยากรณ์จำนวนสายโทรศัพท์เข้าของศูนย์บริการข้อมูลทางโทรศัพท์แบบรายครึ่งชั่วโมง โดยศูนย์บริการข้อมูลทางโทรศัพท์ หรือ Call Center มีบทบาทเป็นศูนย์รวมสายโทรเข้าและโทรออกของธุรกิจ ซึ่งเป็นช่องทางสำคัญในการตอบสนองความต้องการของลูกค้า ไม่ว่าจะเป็นการสอบถามข้อมูล การขอคำแนะนำ หรือแก้ปัญหาต่าง ๆ ศูนย์บริการข้อมูลทางโทรศัพท์จึงมีการจัดวางแผนกำลังคนรับสาย เพื่อให้สอดคล้องกับปริมาณสายโทรศัพท์ที่คาดว่าจะเข้ามา แต่ในบางครั้งการวางแผนจัดกำลังคนรับสายอาจต้องมีการปรับระหว่างวัน เนื่องจากจำนวนสายโทรเข้าอาจมีจำนวนมากกว่าหรือน้อยกว่าที่คาดการณ์ไว้ ซึ่งวิธีการเดิมที่บริษัทใช้ในการคำนวน อาจมีความคลาดเคลื่อน และไม่สามารถปรับตัวเลขได้ภายในระยะเวลาอันสั้น งานวิจัยนี้จึงนำเสนอการพยากรณ์จำนวนสายโทรศัพท์เข้าของศูนย์บริการข้อมูลทางโทรศัพท์แบบรายครึ่งชั่วโมง มีวัตถุประสงค์เพื่อพยากรณ์ปริมาณสายการโทรเข้าช่วงหลัง 10 น. เพื่อช่วยให้ฝ่ายวางแผนกำลังคนเห็นแนวโน้มปริมาณสายที่คาดว่าจะเข้ามา และตัดสินใจปรับแผนการจัดกำลังคนได้อย่างทันท่วงที โดยโมเดลจะจัดกลุ่มและพยากรณ์รูปแบบการกระจายตัวของปริมาณสายโทรเข้า และพยากรณ์จำนวนสายที่คาดว่าจะเข้ามา ผลการทดลองพบว่า โมเดลที่พัฒนาขึ้นมี MAPE อยู่ที่ 20.8% ซึ่งมีประสิทธิภาพดีกว่าวิธีการคำนวนเดิมของบริษัทที่มี MAPE อยู่ที่ 52.7%

การแบ่งส่วนเนื้องอกตับโดยใช้โมเดลการเรียนรู้เชิงลึกด้วยโครงข่ายความสนใจจากรูปภาพสเปคซีที, วันรัฐ ลิ้มประไพพงษ์

Chulalongkorn University Theses and Dissertations (Chula ETD)

การแบ่งส่วนเนื้องอกในตับโดยอัตโนมัติจากภาพถ่ายทางการแพทย์มีบทบาทสำคัญในการช่วยลดภาระงานของรังสีแพทย์ในขั้นตอนการวางแผนรักษามะเร็งตับด้วยวิธีรังสีบำบัด โดยรูปสเปคซีทีมักถูกนำมาใช้เพื่อช่วยระบุส่วนเนื้องอกให้แม่นยำเพื่อให้การวางแผนการรักษามีประสิทธิภาพ อย่างไรก็ตาม การแบ่งส่วนเนื้องอกจากภาพเหล่านี้เป็นเรื่องท้าทายเนื่องจากปัญหาต่างๆ เช่น การกระจายแสงที่ผิดปกติ ทำให้ขนาดเนื้องอกดูใหญ่กว่าความเป็นจริงและลดความแม่นยำในการแบ่งส่วน งานวิจัยฉบับนี้ได้นำเสนอโครงข่ายคัดกรองหลายระดับแบบคู่ (Paired Multiscale Attention Network) ซึ่งเป็นสถาปัตยกรรมที่แบ่งออกเป็นสองทาง เส้นทางแรกฝึกฝนชุดข้อมูลสเปคซีทีโดยใช้โครงข่าย Multiscale Attention Network (MA-Net) เส้นทางที่สองมีการใช้การแปลงแบบไวซ์ท็อปแฮท (White Top-Hat) แล้วนำลักษณะเด่นที่ได้มาควบรวมกับเส้นทางแรก ช่วยลดความบกพร่องของการแบ่งส่วนที่มักเกิดจากความแปรปรวนของแสง ในงานวิจัยนี้จะแบ่งการทดสอบเป็นสองส่วน ส่วนแรกคือการทดสอบโมเดลแบ่งส่วนเนื้อตับ โดยใช้ MA-Net โดยมีโมเดลย่อยคือ ResNet50 ฝึกกับชุดข้อมูลซีทีโดยรวมระหว่างชุดข้อมูลสาธารณะ 3DIRCADb-01 และชุดข้อมูลจากโรงพยาบาลจุฬาลงกรณ์ สภากาชาดไทย โดยมีค่า Dice similarity coefficient (DSC) อยู่ที่ 89.67% ในส่วนการทดสอบโมเดลแบ่งส่วนเนื้องอกตับ จะใช้โครงข่ายคัดกรองหลายระดับแบบคู่ ฝึกด้วยชุดข้อมูล เทคนีเซียม-99 เอ็มเอเอ สเปคซีทีจากโรงพยาบาลจุฬาลงกรณ์ สภากาชาดไทยโดยมีค่า DSC ที่ 67.00% ซึ่งให้ประสิทธิภาพการแบ่งส่วนที่ดีที่สุดเมื่อเทียบกับสถาปัตยกรรมอื่นๆ รวมถึงผลจากงานวิจัยก่อนหน้านี้ที่ทดสอบด้วยชุดข้อมูลเดียวกัน

Securing Internet Of Things (Iot) Data Storage, Savannah Malo

Honors Theses and Capstones

Internet of Things (IoT) devices are commonly known to be susceptible to security attacks, which can lead to the leakage, theft, or erasure of data. Despite similar attack methods used on conventional technologies, IoT devices differ in how they consist of a small amount of hardware, limited networking capability, and utilize NoSQL databases. IoT solutions prefer NoSQL databases since they are compatible for larger datasets, unstructured and time-series data. However, these implementations are less likely to employ critical security features, like authentication, authorization, and encryption. The purpose of this project is to understand why those security measures are not strictly …

Adaptive Neh With Constrained Nearest Neighbor Subtours For The Electric Vehicle Routing Problem With Time Windows, Andrew Struthers

All Master's Theses

The development of electric vehicles is currently considered one of the most innovative areas in manufacturing. Largely driven by the desire to reduce greenhouse emissions, electric vehicles are seen as a viable alternative to internal combustion engine cars. Starting from consumer cars, a dedicated effort is being made to translate this into commercial vehicles for freight and delivery. This research introduces a novel adaptive Nawaz, Enscore, Ham (NEH) algorithm with constrained nearest neighbor subtour (NEH-NN). This algorithm is tested on the standard benchmark problems in literature and used as a seed solution for the Genetic Algorithm (GA). The performance and …

Observability-Aware Path Planning For Autonomous Navigation, Raymond Brink Neistat

Open Access Master's Theses

Terrain-Aided Navigation (TAN) is a popular method of localization for GPS-denied vehicles, particularly in the marine domain. There are many ways to perform TAN in a marine setting, such as Bathymetric SLAM (BSLAM) and Bayesian Filtering with the aid of an a priori map. These techniques have been studied extensively, but show an overall lack of rigorous observability analyses. Without nonlinear observability analyses, TAN practitioners do not have an analytical indicator to know which areas of terrain will provide opportunities for the best localization performance. This thesis reviews current developments in the endeavors of nonlinear observability analyses as well as …

Assessing The Condition Of Pavements (Road Surfaces) Using Computer Vision & Machine Learning, Syed Ibrahim Hassam

Doctoral

Regular inspections of pavements are conducted by civil infrastructure departments to evaluate the surface condition. Pavement surfaces are subject to deterioration caused by several factors such as traffic, weather, and sunlight. This deterioration becomes evident through various distresses, including potholes, rutting, cracking, bleeding, patching, and ravelling, which gradually affects the surface layer over time. It is essential to assess the condition of pavements as it not only ensures their usability but also maximises public safety. Effective pavement maintenance requires substantial resources and capital investment to carry out the most suitable maintenance treatments at the optimal time. Furthermore, the outcomes of …

Designing Ris-Assisted Uav 3d Trajectory Using Deep Reinforcement Learning, Linsong Li

Electronic Theses and Dissertations

Unmanned aerial vehicles (UAVs) are increasingly employed as temporary base stations or access points to facilitate data transfer between ground terminals (GTs). However, in urban environments, UAV-GT communication links often face challenges due to obstructions from buildings and other obstacles, resulting in reduced data transfer efficiency. Reconfigurable intelligent surfaces (RIS) provide a promising solution by reflecting signals to enhance communication quality between UAVs and GTs. This thesis addresses the critical challenge of responsive UAV trajectory optimization in RIS-assisted communication networks. A novel approach is proposed, integrating federated learning with reinforcement learning techniques, specifically Double Deep Q-Network (DDQN) and Deep Deterministic …

Machine Learning For Electronic Structure Prediction, Shashank Pathrudkar

Dissertations, Master's Theses and Master's Reports

Kohn-Sham density functional theory is the work horse of computational material science research. The core of Kohn-Sham density functional theory, the Kohn-Sham equations, output charge density, energy levels and wavefunctions. In principle, the electron density can be used to obtain several other properties of interest including total potential energy of the system, atomic forces, binding energies and electric constants. In this work we present machine learning models designed to bypass the Kohn-Sham equations by directly predicting electron density. Two distinct models were developed: one tailored to predict electron density for quasi one-dimensional materials under strain, while the other is applicable …

Performance Characterization And Optimization Of A Point-Cloud-Based Path Planner In Off-Road Terrains, Casey D. Majhor

Dissertations, Master's Theses and Master's Reports

In this dissertation, I present a multifaceted study on the generation of artificial terrains using a multifractal method, and their use in autonomous ground vehicle (AGV) planning and navigation performance characterization in high-fidelity simulations, and automated parameter optimization.

My first contribution is a multifractal artificial terrain generation method leveraging the 3D Weierstrass-Mandelbrot function to control terrain roughness. We generate 60 unique off-road terrains while varying the fractal dimension and test the impact on vehicle traversal difficulty. Results show that increasing the fractal dimension decreases low-roughness areas, increases semi-rough and high-roughness areas, and decreases vehicle success rates while increasing vertical accelerations, …

Effective Data Augmentation Techniques For Time Series Classification: An Empirical Evaluation, Pongpanod Sankosik

Chulalongkorn University Theses and Dissertations (Chula ETD)

Time series classification is crucial in fields such as healthcare, finance, and industrial processes, but it faces challenges like temporal data ordering, class im-balance, noise, and limited data. This research explores data augmentation techniques to improve classification performance, focusing on the MiniRocket classifier across 85 UCR datasets. The study identifies conditions under which augmentation techniques, like wDBA, enhance accuracy, though overall performance may vary. A dataset-specific approach is essential for effective augmentation. The research also examines the impact of augmentation on datasets with different characteristics, providing insights into when specific strategies are most benefi-cial. Future work includes optimizing augmentation methods …

Sales Forecasting For Retail Business Using Xgboost Algorithm And Timesfm, Prathana Dankorpho

Chulalongkorn University Theses and Dissertations (Chula ETD)

The retail industry is continuously evolving with the expansion of sales channels and the diversification of product assortments. However, current forecasting methods, relying on simplistic statistical models, frequently encounter difficulties in adjusting to the dynamic environment. This limitation leads to challenges in accurately predicting sales. Consequently, there is a critical need to improve the accuracy and frequency of sales predictions to enable timely decision-making for business strategies. Through a comprehensive analysis of datasets from 2019 to 2023, this study illustrates the advantages of integrating XGBoost and TimesFM to gain deeper insights into sales patterns. Results demonstrate a significant enhancement in …

Anonymous Attribute-Based Broadcast Encryption With Hidden Multiple Access Structures, Tran Viet Xuan Phuong

School of Cybersecurity Faculty Publications

Due to the high demands of data communication, the broadcasting system streams the data daily. This service not only sends out the message to the correct participant but also respects the security of the identity user. In addition, when delivered, all the information must be protected for the party who employs the broadcasting service. Currently, Attribute-Based Broadcast Encryption (ABBE) is useful to apply for the broadcasting service. (ABBE) is a combination of Attribute-Based Encryption (ABE) and Broadcast Encryption (BE), which allows a broadcaster (or encrypter) to broadcast an encrypted message, including a predefined user set and specified access policy to …

Evaluating The Performance Of 5g Nr In Indoor Environments: An Experimental Study, Bikash Chandra Singh, Rafael Diaz, Sachin Shetty

School of Cybersecurity Faculty Publications

The 5G wireless standard has emerged as a trans-formative technology with the potential to revolutionize various industries by providing enhanced connectivity and communication capabilities. This advanced standard offers a diverse range of applications, including Ultra-Reliable Low-Latency Communication (URLLC), Enhanced Mobile Broadband (eMBB), and Massive Machine Type Communication (mMTC). In this Scientific research paper, we present a comprehensive analysis of the performance and capabilities of a deployed indoor 5G network in a controlled laboratory environment. The experimental setup comprises an Amarisoft Callbox, serving as the 5G core, along with a Remote Radio Head (RRH) and user equipment (UEs). Our primary objective …

Reinventing Integrated Photonic Devices And Circuits For High Performance Communication And Computing Applications, Venkata Sai Praneeth Karempudi

Theses and Dissertations--Electrical and Computer Engineering

The long-standing technological pillars for computing systems evolution, namely Moore's law and Von Neumann architecture, are breaking down under the pressure of meeting the capacity and energy efficiency demands of computing and communication architectures that are designed to process modern data-centric applications related to Artificial Intelligence (AI), Big Data, and Internet-of-Things (IoT). In response, both industry and academia have turned to 'more-than-Moore' technologies for realizing hardware architectures for communication and computing. Fortunately, Silicon Photonics (SiPh) has emerged as one highly promising ‘more-than-Moore’ technology. Recent progress has enabled SiPh-based interconnects to outperform traditional electrical interconnects, offering advantages like high bandwidth density, …

Fake News Detection In Online Platforms, Elena Shushkevich

Doctoral

This thesis presents research conducted during a Ph.D. program at Technological University Dublin from 2020 to 2024. The objective of this research is to develop and evaluate effective methods for detecting and classifying fake news in social media and press, addressing the critical issue of misinformation in the digital age. The relevance of this study is underscored by the increasing prevalence of fake news and its potential societal impact, emphasizing the importance of advanced tools for identifying and mitigating misinformation.

Detection Of Tooth Position By Yolov4 And Various Dental Problems Based On Cnn With Bitewing Radiograph, Kuo Chen Li, Yi-Cheng Mao, Mu-Feng Lin, Yi-Qian Li, Chiung-An Chen, Tsung-Yi Chen, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Periodontitis is a high prevalence dental disease caused by bacterial infection of the bone that surrounds the tooth. Early detection and precision treatment can prevent more severe symptoms such as tooth loss. Traditionally, periodontal disease is identified and labeled manually by dental professionals. The task requires expertise and extensive experience, and it is highly repetitive and time-consuming. The aim of this study is to explore the application of AI in the field of dental medicine. With the inherent learning capabilities, AI exhibits remarkable proficiency in processing extensive datasets and effectively managing repetitive tasks. This is particularly advantageous in professions demanding …

An Approach For Robotic Pollination That Utilizes Imitation Learning, Ronald Michael Butts Ii

Graduate Theses, Dissertations, and Problem Reports (ETD)

The global decline in pollinator populations poses a significant threat to agriculture, motivating the development of robotic pollination systems. Previous works demonstrated successful robotic pollination of bramble flowers using visual servoing; however, pollination was limited to specific flower orientations. As such, the objective of this work is to develop a robotic pollination system that is capable of pollinating a wider range of orientations.

This research introduces an imitation learning-based framework for robotic pollination that positions the manipulator to view chosen flowers in specific orientations. The developed model leverages object detection (YOLOv8) to identify individual flowers and a convolutional neural network …

Autonomous Object Search Planning In Large-Scale Environments, Matthew A. Collins

Graduate Theses, Dissertations, and Problem Reports (ETD)

The advancement of autonomous search holds significant promise for applications ranging from emergency response to planetary exploration. This thesis investigates strategies to enhance autonomous search performance in large-scale environments. The main contribution of this work is its practical application in real-world scenarios, where efficient search methods are essential for managing vast amounts of data, particularly in large environments. Effective search planning requires navigating complexities such as limited prior information and managing large state spaces, necessitating advanced strategies to plan with this limited information. Additionally, balancing exploration and exploitation is crucial for optimizing the search process, as it ensures thorough coverage …

Enhancing Robotic Exploration Through Semantically-Guided Sampling Strategies, Christopher Alexander Arend Tatsch

Graduate Theses, Dissertations, and Problem Reports (ETD)

From space and deep-sea exploration to disaster response and environmental monitoring, autonomous robots are essential for advancing science, improving safety, and addressing critical challenges. This dissertation introduces a novel open-source strategy for autonomous robotic exploration: the Semantically-Guided Exploration (SGE) framework. Designed for ground vehicles, SGE integrates semantic understanding into the autonomous exploration process, improving decision-making in complex environments. Specifically, the proposed sampling-based approach uses the information from the semantic segmentation of RGB images and depth images to guide the robot's selection of exploration goals. This method enables the robot to steer away from potential dangers such as large rocks and …

Foraging Economies: A Market Based Methodology For Robotic Swarm Foraging, John A. Little

Graduate Theses, Dissertations, and Problem Reports (ETD)

Swarm robotics involves coordinating large groups of autonomous agents to accomplish complex tasks through decentralized, adaptive behaviors, providing a robust and scalable approach suited to dynamic and unpredictable environments. While traditional swarm models frequently draw inspiration from biological systems such as ant colonies or bee foraging, other approaches use techniques from physics, control theory, and economics to achieve effective coordination. This study distinguishes itself by applying economic principles—specifically, market-driven mechanisms like auctions, utility functions based on opportunity cost, and supply-demand dynamics based on fluctuating resource values at a central base—to improve task allocation within a swarm foraging context. This approach …

A New Cache Replacement Policy In Named Data Network Based On Fib Table Information, Mehran Hosseinzadeh, Neda Moghim, Samira Taheri, Nasrin Gholami

VMASC Publications

Named Data Network (NDN) is proposed for the Internet as an information-centric architecture. Content storing in the router’s cache plays a significant role in NDN. When a router’s cache becomes full, a cache replacement policy determines which content should be discarded for the new content storage. This paper proposes a new cache replacement policy called Discard of Fast Retrievable Content (DFRC). In DFRC, the retrieval time of the content is evaluated using the FIB table information, and the content with less retrieval time receives more discard priority. An impact weight is also used to involve both the grade of retrieval …

Methods That Support The Validation Of Agent-Based Models: An Overview And Discussion, Andrew Collins, Matthew Koehler, Christopher Lynch

Engineering Management & Systems Engineering Faculty Publications

Validation is the process of determining if a model adequately represents the system under study for the model’s intended purpose. Validation is a critical component in building the credibility of a simulation model with its end-users. Effectively conducting validation can be a daunting task for both novice and experienced simulation developers. Further compounding the difficult task of conducting validation is that there is no universally accepted approach for assessing a simulation. These challenges are particularly relevant to the paradigm of Agent-Based Modeling and Simulation (ABMS) because of the complexity found in these models’ mechanisms and in the real-world situations they …

Uncertainty Quantification In Large Language Models Through Convex Hull Analysis, Ferhat Ozgur Catak, Murat Kuzlu

Engineering Technology Faculty Publications

Uncertainty quantification approaches have been more critical in large language models (LLMs), particularly high-risk applications requiring reliable outputs. However, traditional methods for uncertainty quantification, such as probabilistic models and ensemble techniques, face challenges when applied to the complex and high-dimensional nature of LLM-generated outputs. This study proposes a novel geometric approach to uncertainty quantification using convex hull analysis. The proposed method leverages the spatial properties of response embeddings to measure the dispersion and variability of model outputs. The prompts are categorized into three types, i.e., ’easy’, ’moderate’, and ’confusing’, to generate multiple responses using different LLMs at varying temperature settings. …

Integrating Knowledge Graphs With Large Language Models For Natural Language Querying, Rakesh Kandula

Browse all Theses and Dissertations

This research explores the integration of knowledge graphs with large language models that have already been trained on a vast pool of unstructured text data. Large language models trained on this type of data have a tendency to hallucinate and produce factually inaccurate results. This behavior is primarily due to the data being trained is unstructured and huge text corpus, and large language model uses predictive text analysis methods to obtain a response. These issues can be addressed by applying Retrieval Augmented Generation and Fine-tuning to large language models, employing an underlying domainspecific knowledge graph. Integrating knowledge graph and large …

Prediction Interpretations Of Ensemble Models In Chronic Kidney Disease Using Explainable Ai, K M Tawsik Jawad

Browse all Theses and Dissertations

Chronic Kidney Disease (CKD) poses significant health and financial threat to millions of patients all around the world. The irreversible nature of this disease not just leads to comorbid diseases like Diabetes Mellitus, Hypertension, Anemia, Bone Disease, Neurological Implants etc. It can permanently damage the kidney by progressing to Acute Kidney Injury (AKI) or End Stage Renal Diseases (ESRD). The risk factors of CKD become more dangerous as patients suffering from it have little to no idea about the presence of CKD in their body until it takes the shape of AKI or ESRD. There are severe economic burdens for …

Managing Inventory With A Database, David Bartlett

Williams Honors College, Honors Research Projects

Large commercial companies often use warehouses to store and organize their product inventory. However, manually keeping track of inventory through physical means can be a tedious process and is at risk for a variety of potential issues. It is very easy for records to be inaccurate or duplicated, especially if large reorganizations are undertaken, as this can cause issues such as duplicate product ID numbers. Therefore, it was decided that an inventory management system utilizing a SQL database should be created. The system needed to have capabilities including allowing the entry of product information, the ability to search database records …

Children's Hospital Animatronic, Erin Keller, Wesley Cunningham, Dylan Mueller, Carl Richter

Williams Honors College, Honors Research Projects

This senior project explores the development of a pediatric-oriented animatronic designed to enhance the hospital experience for children. Recognizing the importance of alleviating the anxiety and fear that often accompany hospital visits, the project focuses on creating an engaging and interactive companion to soothe children.

Key components of this project include:

1. Creative Design: The animatronic character's design prioritizes child-friendliness, employing research into child psychology and preferences to ensure an appealing and approachable aesthetic.

2. Electrical and Mechanical Engineering: A robust mechanical and electronic system was engineered to enable lifelike movements, gestures, and responses.

3. Interactive Features: A simple, user-friendly app will be …

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Development Of A Solidworks Api, Andrew Magyar

Williams Honors College, Honors Research Projects

With the current advancements in CAD software, and precision of modern motors, we believe that a new way to communicate from hardware to software is possible. This report details the steps we have taken to attempt just that. The essential idea behind this is that modern controls for robotics are not as user friendly as they can be. The general action taken is to create an API for SolidWorks that connects it to the other necessary controller software and makes it so that a robot, and its exact current position, can be changed simply and intuitively from SolidWorks. Furthermore, the …