Digital Commons Network^™

การปลูกและวิเคราะห์ลักษณะสมบัติของฟิล์มแกลเลียมอาร์เซไนด์ฟอสไฟด์บิสไมด์บนแกลเลียมอาร์เซไนด์และฟิล์มอินเดียมฟอสไฟด์บิสไมด์บนแกลเลียมแอนติโมไนด์, สหรัฐ ชมเดช

Chulalongkorn University Theses and Dissertations (Chula ETD)

วิทยานิพนธ์ฉบับนี้นำเสนอการปลูกชั้นฟิล์ม GaAsPBi บนแผ่นฐาน GaAs เพื่อลดความบกพร่องของโครงผลึกในชั้นฟิล์ม และชั้นฟิล์ม InPBi บนแผ่นฐาน GaSb เพื่อให้เกิดการดึง Bi เข้ามาผสมในชั้นฟิล์มมากขึ้น ชิ้นงานถูกปลูกโดยเทคนิคเอพิแทกซีลำโมเลกุล (MBE) นอกจากนี้ชิ้นงาน GaAsPBi ถูกนำไปอบที่อุณหภูมิสูง เพื่อศึกษาผลกระทบของอุณหภูมิที่มีต่อคุณสมบัติของวัสดุ ชิ้นงาน GaAsPBi ถูกศึกษาสัณฐานวิทยาด้วยกล้องจุลทรรศน์แรงอะตอม (AFM) จุลทรรศน์แบบใช้แสง (OM) และ กล้องจุลทรรศน์อิเล็กตรอนแบบส่องกราด (SEM) ถูกศึกษาโครงสร้างผลึกด้วยเทคนิค XRD ถูกศึกษา oxidation state และองค์ประกอบของธาตุในชั้นฟิล์มด้วยเทคนิคการดูดกลืนรังสีเอกซ์ (XAS) และถูกศึกษาคุณสมบัติเชิงแสงด้วยเทคนิค PL ชิ้นงาน InPBi ถูกศึกษาสัณฐานวิทยาด้วยกล้องจุลทรรศน์แรงอะตอม (AFM) ถูกศึกษาโครงสร้างผลึกด้วยเทคนิค XRD และถูกศึกษาคุณสมบัติเชิงแสงด้วยเทคนิค PL. ผลการวิเคราะห์ชิ้นงาน GaAsPBi แสดงว่าชิ้นงานที่ได้รับการปรับปรุงเงื่อนไขการปลูก ไม่มีความบกพร่องของโครงผลึกบนผิวหน้า เนื่องจากชั้นฟิล์มมีค่าคงตัวแลตทิซใกล้เคียงกับแผ่นฐาน มีปริมาณของ Bi ในชั้นฟิล์มเพิ่มขึ้น และมีปรับปรุงผลตอบสนองเชิงแสงให้ดีขึ้น การศึกษาผลของการอบชิ้นงานพบว่า การอบที่ 650°C เกิดการทำลายผิวหน้าของชิ้นงาน ทำให้คุณภาพผลึกแย่ลง การอบช่วยเพิ่มประสิทธิภาพการเปล่งแสงได้เล็กน้อยในกรณีการอบที่อุณหภูมิ 450°C และ 550°C แต่การอบที่ 650°C ทำให้ประสิทธิภาพการเปล่งแสงลดลงไปอย่างมาก และการอบไม่ส่งผลต่อองค์ประกอบในชั้นฟิล์ม ชั้นฟิล์ม InPBi มีโครงผลึกที่มีการคลายความเครียด ทำให้ผิวหน้ามีความขรุขระ ความขรุขระของผิวหน้าและปริมาณของ Bi ในชั้นฟิล์ม ถูกพบว่าแปรผันกับอุณหภูมิปลูกและปริมาณของ Bi ในลำโมเลกุล ผลตอบสนองเชิงแสงของชิ้นงานแสดงยอดการเปล่งแสงหลายยอด ชิ้นงานที่มีปริมาณ Bi มากที่สุดถูกปลูกที่อุณหภูมิ 300°C โดยมีปริมาณ Bi ในลำโมเลกุลเป็น 2×10-8 Torr ซึ่งทำให้มี Bi ในชั้นฟิล์มเป็น 2.45% เมื่อเปรียบเทียบกับชั้นฟิล์มที่ปลูกด้วยเงื่อนไขเดียวกันบนแผ่นฐาน InP แล้ว พบว่ามีปริมาณ Bi ที่มากกว่าเล็กน้อยในชั้นฟิล์มที่ปลูกบน GaSb

Phase-Variation Microwave Resonator For Highly Sensitive Dynamic Sensing, Chen Zhu, Rex E. Gerald, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

High-precision dynamic sensing is critical in fields, such as industrial automation, structural health monitoring, and environmental sensing, where real-time responses to minuscule changes can prevent system failures or optimize performance. In this work, we introduce and demonstrate a phase-variation coaxial cable resonator (CCR) as a highly sensitive sensor for dynamic sensing applications. As a proof of concept, a prototype device based on a custom-designed CCR is thoroughly investigated for dynamic displacement measurements, as displacement is a fundamental quantity essential to numerous applications. The sensor consists of two components: a static CCR device and a movable conducting plate. As the conducting …

Radiated Susceptibility Testing Using Near-Field Scanning, Mckennan Starkey, Aaron Harmon, Cody Goins, Kristen Donnell, Victor Khilkevich, Daryl Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

Determining locations and components in a system responsible for radiated coupling is challenging. Methods, such as near field injection susceptibility scanning or direct power injection, can only find locations and frequencies where a component is sensitive to the near field or to an injected signal but cannot deduce if the component is well coupled to the far-field. In this paper, a method to experimentally determine the levels of radiated coupling within a target system is proposed. A near-field differential loop probe is scanned over the target device while measuring the radiated energy in a stirred-mode tent. By sweeping the near …

Age Of Information-Based Optimal Scheduling With Energy Cost Trade-Off For Smart Warehouse: A Deep Reinforcement Learning-Based Approach, Sandip Roy, Abhishek Bisht, Ashok Kumar Das, Sachin Shetty

VMASC Publications

Recent advances in the integration of high-speed mobile networks and real-time IoT devices have facilitated in building of smart warehouses, where a set of beacons and Internet of Things (IoT) devices (or source nodes) can monitor the status of various physical processes in a time-critical way. In real-time status monitoring systems, like smart warehouses, quantifying the freshness of the Internet of Things (IoT) data based on the age of information (AoI) metrics becomes quite crucial. As source nodes are battery-constrained, a balanced trade-off between AoI minimization and preservation of source node battery energy is essential. In this paper, in a …

Faithful Narratives From Complex Conceptual Models: Should Modelers Or Large Language Models Simplify Causal Maps, Tyler J. Gandee, Philippe J. Giabbanelli

VMASC Publications

(1) Background: Comprehensive conceptual models can result in complex artifacts, consisting of many concepts that interact through multiple mechanisms. This complexity can be acceptable and even expected when generating rich models, for instance to support ensuing analyses that find central concepts or decompose models into parts that can be managed by different actors. However, complexity can become a barrier when the conceptual model is used directly by individuals. A ‘transparent’ model can support learning among stakeholders (e.g., in group model building) and it can motivate the adoption of specific interventions (i.e., using a model as evidence base). Although advances in …

A Comparative Analysis Of Preprocessing Filters For Deep Learning-Based Equipment Power Efficiency Classification And Prediction Models, Sang-Ha Sung, Chang-Sung Seo, Michael Pokojovy, Sangjin Kim

Mathematics & Statistics Faculty Publications

The quality of input data is critical to the performance of time-series classification models, particularly in the domain for industrial sensor data where noise and anomalies are frequent. This study investigates how various filtering-based preprocessing techniques impact the accuracy and robustness of a Transformer model that predicts power efficiency states (Normal, Caution, Warning) from minute-level IIoT sensor data. We evaluated five techniques: a baseline, Simple Moving Average, Median filter, Hampel filter, and Kalman filter. For each technique, we conducted systematic experiments across time windows (360 and 720 min) that reflect real-world industrial inspection cycles, along with five prediction offsets (up …

Assessing Tidal Energy Potential In The Visayas: Viability Of The San Bernardino, San Juanico, And Cebu Straits, Justin Kyle O. Ricafort, King Harold A. Recto

Sustainability, Disaster Science and Energy

By laying the groundwork for sustainable tidal energy infrastructure, this study contributes to advancing the Philippines' renewable energy portfolio and supports the global transition to clean energy solutions. With the country's extensive coastline and rising energy demands, tidal energy presents a largely underutilized yet promising resource that can address both local and global energy challenges. Tidal energy is highly predictable, stable, and environmentally friendly, offering a reliable alternative to conventional energy sources like coal and natural gas, which are often subject to price volatility and environmental concerns. The study focuses on the Visayas region, a prime candidate for tidal energy …

Study Of Deep Neural Network Trained With Salient, Compressed Medical Video Data For Enhanced Predication, Aileen Sengupta

Electrical Engineering Dissertations - Archive

The rapid growth of surgical video analysis presents a need for efficient deep learning models for surgical training, while reducing the need for excessive image and video image storage. Traditional training approaches typically rely on uniformly compressed video data, instead of selectively preserving the most surgically relevant regions. This dissertation investigates the impact of training deep neural networks (DNNs), both convolutional and transformer-based architectures, on saliency-guided, differentially compressed surgical video sequences. The study systematically evaluates how such compression influences prediction accuracy, computational efficiency and storage requirements. Experimental results demonstrate that models trained on ROI-focused compressed data combined with motion vectors …

Hardware Implementation Of An Efficient Frequency Comb Generator For Microwave Kinetic Inductance Detectors, Frank Atobrah Danso

Open Access Master's Theses

The frequency comb generator is an essential component in the readout of superconducting detectors. This component is responsible for generating the resonant frequencies necessary to detect photon interaction with the detectors. This component however has not received the much needed attention in terms of readout systems optimization. Conventional implementations rely on either inverse fast Fourier transform (IFFT) engines, which scale poorly in power with increasing frequency resolution requirement but allow flexible tone updates, or lookup tables (LUTs), which demand large memory for high frequency resolution.

Microwave Kinetic Inductance Detectors (MKIDs) are superconducting detectors which have gained significant interest for cryogenic …

Comparative Study Of Physiological Signals Between The Embraceplus And The Shimmer3 Gsr+, Cassidy Considine

Open Access Master's Theses

Understanding team dynamics and their physiological impact is essential across high-stakes industries such as healthcare, aviation, and defense. In stressful environments, optimizing team performance requires not only assessing communication but also monitoring physiological indicators of stress. Electrodermal activity (EDA) and heart rate variability (HRV) are commonly used to capture autonomic responses, offering insight into emotional arousal and cardiovascular regulation.

This pilot study compares a commercial wearable device, the EmbracePlus, with a research-grade device, the Shimmer3 GSR+, to evaluate differences in physiological signal acquisition. Twelve participants completed a five-phase experimental protocol while wearing both devices simultaneously on the non-dominant wrist/forearm. EDA …

Safety Monitoring & Intelligent Power Distribution Systems For Compact Agricultural Equipment, Mason O. Bradley

Theses and Dissertations--Biosystems and Agricultural Engineering

The agricultural industry continues to be one of the most dangerous career fields in the United States of America with compact agricultural equipment being a common source for casualties in the industry. The primary focus of this work is to develop new technology that improves operational safety of compact agricultural equipment through the use of low-cost embedded electronics. This work presents the design of three systems in alignment with this goal.

The first system aims to integrate electrical intelligence and passive safety monitoring into the power distribution system of a small piece of agricultural equipment. The system known as the …

High-Performance Computational Kernels For Algorithm-System Co-Design In Machine Learning: Enabling Efficient Gcn Training And Llm Inference Serving., Shakya D. Jayakody Arachchige

Graduate Thesis and Dissertation post-2024

The rapid advancement of machine learning, from Graph Convolutional Networks (GCNs) to transformer-based Large Language Models (LLMs), continues to expose fundamental limitations in memory efficiency, scalability, and system reliability. GCNs are critical for domains such as biomedical modeling, social networks, and recommendation systems, yet their reliance on sparse general matrix–matrix multiplication (SpGEMM) makes them highly sensitive to GPU memory constraints and irregular access patterns. As graph data scales, out-of-core computation becomes inevitable, but existing systems are hindered by I/O bottlenecks and underutilized GPU resources. Our contribution with AIRES demonstrates how algorithm–system co-design can alleviate these challenges: by introducing block-wise data …

Ai-Generated Messaging For Life Events Using Structured Prompts: A Comparative Study Of Gpt With Human Experts And Machine Learning, Christopher Lynch, Erik Jensen, Ross Gore, Virginia Zamponi, Kevin O'Brien, Brandon Feldhaus, Katherine Smith, Joseph Martínez, Madison H. Munro, Timur E. Ozkose, Tugce B. Gundogdu, Ann Marie Reinhold, Hamdi Kavak, Barry Ezell

VMASC Publications

Large Language Models (LLMs) play an increasingly integrated and pivotal role in generating diverse types of texts, such as social media messages, emails, narratives, and technical reports, among other textual communication forms. As AI-generated messaging filters into human communication, a systematic exploration of their effectiveness for mimicking human-like communication of life events is needed. In this study, we employ a zero-shot structured narrative prompt to generate 24,000 life event messages for birth, death, hiring, and firing events using OpenAI's GPT-4. From this dataset, we manually classify 2880 messages and evaluate their validity in conveying these life events through the form …

High Voltage Circuit Breaker Arc Modelling State Of Art, Raghdaa I. Elbahy, Ahmed M. Abdelbaset, Ebrahim A. Badran, Mansour H. Abdel-Rahman

Mansoura Engineering Journal

High-voltage (HV) circuit breakers (CBs) are critical for safe and reliable power system operation. However, electric arcs formed during current interruption pose a challenge due to their complex, non-linear behavior. These arcs can hinder CB efficiency and cause equipment damage. To address this, understanding and modeling arc properties are essential. This paper explores existing methods for modeling and simulating electric arcs in HV CBs, including physical, black box, and parameter models. By analyzing these approaches, the paper emphasizes the importance of arc modeling for CB optimization and outlines the physical, mathematical, and software requirements for effective modeling and simulation.

Time Series Forecasting With Lstm: An Extensive Content Analysis, Andri Pranolo, Xiaofeng Zhou, Yingchi Mao

Knowledge Engineering and Data Science

This paper presents a comprehensive bibliometric and content review of the trend, architecture, and application of long short-term memory (LSTM) models for time series forecasting. The study aims to provide insights into the overall statistics and distribution of papers focused on LSTM for forecasting. Additionally, the research questions address the most highly cited papers based on LSTM approaches in forecasting, the most productive journals in this field, and identifying trends, gaps, summary tasks and their performance, datasets availability, and future research directions for LSTM in forecasting. This paper is a comprehensive review of LSTM for forecasting from 2017 to 2023 …

Prediction Of Audit Findings Using Deep Learning With Financial And Non-Financial Data: A Case Study In Province X, Fery Yohan Setiawan, Eko Mulyanto Yuniarno, Reza Fuad Rachmadi

Knowledge Engineering and Data Science

The implementation of the audit from the local government financial statements by The Audit Board of The Republic of Indonesia (BPK RI), especially for the Province X representative, are frequently faced by the various limitations, one of them being the required audit time. At this moment, the BPK RI representative of Province X doesn’t have the tools that are able to help the accurate of sample determination for the pick test, which resulted in this study proposing the application of multi-label classification to predict the findings of financial statement (Laporan Keuangan, LK) audits based on financial and non …

Cognitive Eeg Differentiation With Hypnosis-Based Noise Reduction And K-Harmonic Means For Personalized Brainwave Modeling, Ahmad Azhari, Dimas Chaerul Ekty Saputra

Knowledge Engineering and Data Science

This study investigates the integration of hypnosis-based noise reduction and K-Harmonic Means (KHM) clustering for personalized brainwave modeling using Electroencephalography (EEG) data. EEG signals were collected from 100 participants using a Neurosky Mindset sensor at the FP1 (prefrontal) location, with each subject performing nine standardized cognitive tasks such as breathing, memory recall, and mathematical problem-solving. Hypnosis was applied not as a filtering method but as a behavioral protocol to standardize subject conditions and minimize physiological and environmental noise. The EEG signals were sampled at 128 Hz and analyzed using KHM clustering with K=4K = 4K=4, resulting in a Silhouette Score …

Design And Control Of A Stroke Therapy Device, Hugh Elliott

Open Access Master's Theses

Stroke is a leading cause of physical disability around the world, and the likelihood of stroke increases as people live longer. The current number of physiotherapists is insufficient to meet the increasing demand for their services. As a result, there has been a focus on developing robotic devices that function similarly to traditional therapy, enabling multiple patients to be seen simultaneously. While many devices have been created and tested, most are expensive, complex, and require trained personnel for supervision, thereby limiting their outreach. This thesis presents the design and control of a low-cost stroke therapy device designed to promote upper …

Smart Qos-Aware Resource Management For Edge Intelligence Systems, Minoo Hosseinzadeh

Theses and Dissertations--Computer Science

There are several definitions for Smart Cities. One common key point of these definitions is that smart cities are technologically advanced cities which connect everything in a complex urban environment including infrastructure, information, and even people to cope with the crucial problems linked with the urban life such as traffic, pollution, city crowding, health, and poverty. Central to this vision are the Internet of Things (IoT) and Big Data, where interconnected devices with sensors collect vast amounts of data for informed decision-making. However, the rapid expansion of IoT devices challenges efficient data processing while meeting diverse Quality-of-Service (QoS) requirements; for …

Twitter-Based Osint For Cyber Event Analytics, Dakota S. Dale, Kylie Mcclanahan, W. Sky Elder, Qinghua Li

Electrical Engineering and Computer Science Faculty Publications and Presentations

Open-Source Intelligence (OSINT) is often regarded as a critical component for cybersecurity intelligence gathering to secure cyber infrastructures. As Artificial Intelligence (AI) and social media progress and become more commonplace, we have the unique opportunity to collect and analyze information from social media in real time. In this paper, we propose an AI-based framework for automatically filtering the Twitter stream for cybersecurity information, analyzing the natural language to aggregate the tweets around specific cybersecurity events, and certifying the information. The system's applications range from discovering events security operators may not have been aware of to helping operators investigate on-going events …

Neurovascular Coupling Impairments In Acute Traumatic Brain Injury: An Eeg-Nirs Analysis, Zachary Armstrong

Bioengineering Theses - Archive

Traumatic brain injury (TBI) is a major cause of neurological impairment, often leading to variable recovery and uncertain prognosis in the neurocritical care setting. There is a pressing clinical need for robust, physiologically grounded biomarkers to inform prognosis and therapeutic decision-making in acute TBI. This thesis investigates neurovascular coupling (NVC), the physiological coordination between neuronal activity and cerebral blood flow, as a candidate biomarker for brain function and recovery after injury.

A prospective cohort study was performed using simultaneous electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings in patients with moderate-to-severe TBI and healthy controls. Wavelet transform coherence (WTC) analysis was …

Energy-Aware Swarm Robotics In Smart Microgrids Using Quantum-Inspired Reinforcement Learning, Mohamed Shili, Salah Hammedi, Hicham Chaoui, Khaled Nouri

Electrical & Computer Engineering Faculty Publications

The integration of autonomous robots with intelligent electrical systems introduces complex energy management challenges, particularly as microgrids increasingly incorporate renewable energy sources and storage devices in widely distributed environments. This study proposes a quantum-inspired multi-agent reinforcement learning (QI-MARL) framework for energy-aware swarm coordination in smart microgrids. Each robot functions as an intelligent agent capable of performing multiple tasks within dynamic domestic and industrial environments while optimizing energy utilization. The quantum-inspired mechanism enhances adaptability by enabling probabilistic decision-making, allowing both robots and microgrid nodes to self-organize based on task demands, battery states, and real-time energy availability. Comparative experiments across 1500 grid-based …

Are We Ready For Synchronous Conceptual Modeling In Augmented Reality? A Usability Study On Causal Maps With Hololens 2, Anish Shrestha, Philippe J. Giabbanelli

VMASC Publications

(1) Background: Participatory modeling requires combining individual views to create a shared conceptual model. While remote collaboration tools have enabled synchronous online modeling, they are limited to desktop settings. Augmented reality (AR) offers a new approach by potentially providing the sense of presence found in physical collaboration, which may better support participants in achieving the sense of presence found in physical locations, thus supporting them in negotiating meaning and building a shared model. (2) Methods: Building on prior works that developed technology, we performed a usability study with pairs of modelers to examine their ability at performing key conceptual modeling …

Fabrication Of Thylakoid Membrane-Based Photo-Bioelectrochemical Bioanode For Self-Powered Light-Driven Electronics, Amit Sarode, Gymama Slaughter

Center for Bioelectronics Publications

The transition toward sustainable and decentralized energy solutions necessitates the development of innovative bioelectronic systems capable of harvesting and converting renewable energy. Here, we present a novel photo-bioelectrochemical fuel cell architecture based on a biohybrid anode integrating laser-induced graphene (LIG), poly(3,4-ethylenedioxythiophene) (PEDOT), and isolated thylakoid membranes. LIG provided a porous, conductive scaffold, while PEDOT enhanced electrode compatibility, electrical conductivity, and operational stability. Compared to MXene-based systems that involve complex, multi-step synthesis, PEDOT offers a cost-effective and scalable alternative for bioelectrode fabrication. Thylakoid membranes were immobilized onto the PEDOT-modified LIG surface to enable light-driven electron generation. Electrochemical characterization revealed enhanced redox …

Ecobuoys For Scalable Oceanography, Anuscheh Nawaz, Michael Steele, Ruth Branch, David Burnett, Kuotian Liao, Mallory Parker, Eleftheria Roumeli

Electrical and Computer Engineering Faculty Publications and Presentations

An approach to scalable surface-drifting buoys is needed to enable the high spatial and temporal resolution of oceanographic data that the science and meteorological communities are asking for. With the number of active buoys predicted to increase by a factor of 100 or more, the impact on the environment becomes even more important. Here, we present a pathway to a scalable and sustainable generation of buoys. We identify the main criteria to be used when developing such buoys to be low cost, with reliable data and neutral or even positive environmental impact. For each buoy subsystem—hull, electronics, energy generation and …

Estimating Electric Power Consumption Of Grid-Enabled Residential Heat Pump Water Heaters, Dana Paresa, Robert Bass

Electrical and Computer Engineering Faculty Publications and Presentations

We present two methods for estimating compressor electric power consumption of residential heat pump water heaters. Power is calculated using data from voltage and current instrumentation, which add costs and potential failure points to the unit. The two methods presented herein use thermal instrumentation or derived data that are typically included within modern residential heat pump water heaters. One method uses data from lower and upper tank temperature sensors. The other method uses an attribute that is commonly available within grid-enabled flexible load heat pump water heaters. For both methods, fit equations were derived, for which coefficients are tuned to …

Grid Services Demonstration Using Service-Oriented Derms, Midrar Adham, Sean Keene, Tylor E. Slay, Jaime T. Kolln, Robert Bass

Electrical and Computer Engineering Faculty Publications and Presentations

This work demonstrates four grid service use cases using a service-oriented DER Management System within an Energy Grid of Things network. Imposed by a set of rules referred to as the Energy Service Interface, the DER Management System uses IEEE 2030.5 as its primary communication protocol. To simulate the efforts of Grid Operators, Grid Service Providers, and consumers to provide grid services, a co-simulation environment that leverages the GridAPPS-D simulation platform was developed. The results show that a service-oriented DER Management System provides a means of achieving reliable and impactful grid services while protecting customers’ privacy and ensuring their agency …

Design And Optimization Of A Miniaturized Spiral Antenna For Ultra-Wideband Applications, Mckennan Starkey, Cody Goins, Victor Khilkevich, Daryl Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

Ultra-wideband communication in the frequency range from 3.1 to 10.6 GHz is popular for short range wireless networks. Designing a wideband antenna that covers the entire 7.5 GHz band is challenging, particularly if the antenna should be small, unobtrusive, and should effectively communicate with another antenna as their positions and orientations change. A miniaturized wideband circularly polarized Archimedean spiral antenna is developed in this study which is wideband and circularly polarized. Miniaturization and optimization techniques are introduced to enable an antenna size of $30 \text{mm} \times 30 \text{mm}$ and a thickness less than 0.5 mm.

Zips Racing Electric - Vehicle Control Unit, Ryan Stoller, Brian Glen, John Wozencraft, Tetra Engdahl

Williams Honors College, Honors Research Projects

Zips Racing Electric is a design team at the university that builds and races electric cars. The racing team wants to design a four-wheel drive system to increase performance, but the current computer on the vehicle cannot control the more complex power train. The goal of the project will be to build a hardware device that can read sensor inputs and control the torque output to each motor individually. The control device being designed will need to interface with the existing systems on the car and control regenerative breaking, torque vectoring, and other auxilliary systems on the car.

Voice Operated Automatic Chessboard, Logan Pursley, Rafael Soler, Jason Roser, Tyler Hoxworth

Williams Honors College, Honors Research Projects

Chess is a board game played at a variety of skill levels worldwide. Over 1000 years, chess has always required players to move the pieces manually. Unfortunately, anyone who lacks dexterity in their hands or arms cannot manually move the chess pieces on their own. Consequently, a chess board that can be played without hands or arms must be created to accommodate a broader audience. This is the motivation behind the Automatic Chessboard. This design will allow a player to engage in a dynamic and physical game of chess using only their voice. A 3-axis control system will allow for …