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DESCRIPTION:UC\, MU\, and NKU Senior Design/Capstone Projects\n\nThis meeti
 ng will feature presentations on select senior projects by students from U
 niversity of Cincinnati\, Miami University\, and Northern Kentucky Univers
 ity. Each year the engineering students complete their senior projects. We
  are amazed at the talent and creativity of these students as they present
  their projects to us. These presentations are typically an interesting mi
 x of hardware and software\, solving problems\, improving efficiencies\, a
 nd creating new opportunities.\n\nABOUT THE PRESENTERS:\n\nMiami Universit
 y\n\nProject Title: Autonomous Omni-Directional Automated Guided Vehicle (
 AGV)\n\nTeam Members: Seth Burghard\, Nick Delaet\, and Mason Powers\n\nFa
 culty Advisor: Dr. Mahdi Yazdanpour\n\nAbstract: The objective of this pro
 ject is to design\, prototype\, and demonstrate an autonomous omni-directi
 onal Automated Guided Vehicle (AGV) capable of navigating both indoor and 
 controlled outdoor environments on a single-floor surface. The AGV will ut
 ilize a holonomic drive system using Mecanum wheels\, a LiDAR sensor for S
 imultaneous Localization and Mapping (SLAM)\, and an NVIDIA Jetson Nano ru
 nning the Robot Operating System (ROS) for localization\, path planning\, 
 and obstacle avoidance.\n\nProject Title: Virtual Twinning Progression to 
 Analyze/Predict Circuit Failure\n\nTeam Members: Logan Liu\, Sam Shuman\, 
 and Sean Whyle\n\nFaculty Advisor: Dr. Mark Scott and Dr. Peter Jamieson\n
 \nAbstract: Capacitors play critical roles in power conversion application
 s. Yet they are among the components most likely to fail. This project val
 idates a methodology created to observe failing capacitors. It created a m
 odel using both old and new experimental data and verified its accuracy.\n
 \nProject Title: Online Programmable Logic Controller Course\n\nTeam Membe
 rs: Charlize Hadix\, Philip Hampton\, Carter Smith\, and Brandon Vu\n\nFac
 ulty Advisor: Dr. Mark Scott and Jim Leonard\n\nAbstract: Our project is d
 edicated to providing a hybrid course on Programmable Logic Controllers (P
 LCs) for Miami University students. The course covers various topics regar
 ding PLCs such as its history\, safety\, programming\, I/O\, etc. to allow
  students to be prepared for PLC use in an industrial environment. Hands-o
 n labs were also implemented to help students further develop the skills n
 ecessary for operation as well as gain practical experience with PLCs.\n\n
 University of Cincinnati\n\nProject Title: Ensuring UAS Airworthiness: Dee
 p Learning Based Acoustic Health Monitoring of Motor Health\n\nTeam Member
 s: Siddharth Urankar\, Prissha Chawla\n\nFaculty Advisor: Dr. Manish Kumar
 \n\nAbstract: This work presents an inflight UAV powertrain health monitor
 ing framework using machine learning for real time anomaly detection. We c
 ollected high fidelity acoustic signatures from Brushless DC motors to dev
 elop a semi supervised 1D Convolutional Neural Network Autoencoder. By tra
 ining exclusively on healthy acoustic profiles\, the system identifies mec
 hanical degradation by analyzing reconstruction error thresholds during fl
 ight. This non invasive approach supports preflight checks and active moni
 toring within the In time Aviation Safety Management System (IASMS) to ens
 ure airworthiness in mission critical environments.\n\nProject Title: UC N
 avvy\n\nTeam Members: Elaine Mansour\, Justin Lin\, Karr Stump\n\nFaculty 
 Advisor: Giovani Abuaitah\n\nAbstract: UC-Navvy is an indoor and outdoor c
 ampus navigation system designed specifically for the University of Cincin
 nati. The system provided interactive map-based wayfinding across 46 campu
 s buildings\, turn-by-turn routing instructions\, walk time estimates\, an
 d a dedicated ADA-accessible routing mode that restricted paths to elevato
 r- and ramp-equipped corridors. The application was built using React\, Ty
 peScript\, and MapLibre GL for the web platform\, and was wrapped in Capac
 itor for native iOS and Android deployment\, delivering a unified cross-pl
 atform experience with no dependency on proprietary mapping SDKs. Routing 
 was powered by a custom Dijkstra implementation operating on a GeoJSON nav
 igation graph of the UC campus. The project addressed a gap left by mainst
 ream tools\, namely the absence of ADA-aware campus routing and turn-by-tu
 rn indoor/outdoor wayfinding for University of Cincinnati students\, facul
 ty\, and visitors.\n\nNKU\n\nTitle: Knowledge Distillation from Large Reas
 oning Models to Compact Student Language Models\n\nTeam Members: Gaurab Ba
 ral\, Aaditya Khanal\n\nFaculty Advisor: Dr. Yangyang Tao\n\nAbstract: Thi
 s project explores knowledge distillation from the large reasoning model D
 eepSeek-R1 to the compact Qwen2.5-7B using problems from the John O’Brya
 n Mathematics Competition (2011–2025). A Chain-of-Thought dataset was ge
 nerated through a dual-agent framework and used to fine-tune the student m
 odel via LoRA on Apple Silicon with the MLX framework. Early stopping at 2
 00 iterations mitigated overfitting\, improving accuracy from 64.67% to 69
 .43%. The fine-tuned model also demonstrated strong generalization\, achie
 ving 73.1% accuracy on the MATH-500 benchmark. A key advantage is that the
  distilled Qwen2.5-7B model can be deployed on devices such as mobile phon
 es or Raspberry Pi systems\, enabling offline mathematical reasoning witho
 ut internet access.\n\nRoom: Brewhouse\, March First Brewing &amp; Distilling\
 , 7885 E Kemper Rd\, Cincinnati\, Ohio\, United States\, 45249
LOCATION:Room: Brewhouse\, March First Brewing &amp; Distilling\, 7885 E Kemper
  Rd\, Cincinnati\, Ohio\, United States\, 45249
ORGANIZER:dave@arcflashbrokerage.com
SEQUENCE:17
SUMMARY:IEEE Cincinnati April 2026 Meeting
URL;VALUE=URI:https://events.vtools.ieee.org/m/551200
X-ALT-DESC:Description: &lt;br /&gt;&lt;p style=&quot;line-height: 1.4\;&quot;&gt;&lt;span style=&quot;co
 lor: rgb(0\, 0\, 0)\; background-color: rgb(241\, 196\, 15)\;&quot;&gt;&lt;strong&gt;UC\
 , MU\, and NKU Senior Design/Capstone Projects&lt;/strong&gt;&lt;/span&gt;&lt;/p&gt;\n&lt;div s
 tyle=&quot;line-height: 1.4\;&quot;&gt;\n&lt;p&gt;This meeting will feature presentations on 
 select senior projects by students from University of Cincinnati\, Miami U
 niversity\, and Northern Kentucky University.&amp;nbsp\; Each year the enginee
 ring students complete their senior projects.&amp;nbsp\; We are amazed at the 
 talent and creativity of these students as they present their projects to 
 us. These presentations are typically an interesting mix of hardware and s
 oftware\, solving problems\, improving efficiencies\, and creating new opp
 ortunities.&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;&lt;u&gt;ABOUT THE PRESENTERS:&lt;/u&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;
 &lt;strong&gt;Miami University&lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Project Title:&lt;/strong&gt;&amp;
 nbsp\;Autonomous Omni-Directional Automated Guided Vehicle (AGV)&lt;/p&gt;\n&lt;p&gt;&lt;
 strong&gt;Team Members:&lt;/strong&gt;&amp;nbsp\;Seth Burghard\, Nick Delaet\, and Maso
 n Powers&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Faculty Advisor:&lt;/strong&gt;&amp;nbsp\;Dr. Mahdi Yazdanp
 our&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt;&amp;nbsp\;The objective of this project
  is to design\, prototype\, and demonstrate an autonomous omni-directional
  Automated Guided Vehicle (AGV) capable of navigating both indoor and cont
 rolled outdoor environments on a single-floor surface. The AGV will utiliz
 e a holonomic drive system using Mecanum wheels\, a LiDAR sensor for Simul
 taneous Localization and Mapping (SLAM)\, and an NVIDIA Jetson Nano runnin
 g the Robot Operating System (ROS) for localization\, path planning\, and 
 obstacle avoidance.&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;&amp;nbsp\;&lt;/strong&gt;&lt;strong&gt;Project Title:
 &lt;/strong&gt;&amp;nbsp\;Virtual Twinning Progression to Analyze/Predict Circuit Fa
 ilure&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Team Members:&lt;/strong&gt;&amp;nbsp\;Logan Liu\, Sam Shuman\
 , and Sean Whyle&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Faculty Advisor:&lt;/strong&gt;&amp;nbsp\;Dr. Mark 
 Scott and Dr. Peter Jamieson&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt;&amp;nbsp\;Capa
 citors play critical roles in power conversion applications.&amp;nbsp\; Yet th
 ey are among the components most likely to fail.&amp;nbsp\; This project valid
 ates a methodology created to observe failing capacitors.&amp;nbsp\; It create
 d a model using both old and new experimental data and verified its accura
 cy.&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Project Title:&lt;/strong&gt;&amp;nbsp\;Online Programmable Logi
 c Controller Course&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Team Members:&lt;/strong&gt;&amp;nbsp\;Charlize 
 Hadix\,&amp;nbsp\;Philip Hampton\, Carter Smith\, and Brandon Vu&lt;/p&gt;\n&lt;p&gt;&lt;stro
 ng&gt;Faculty Advisor:&lt;/strong&gt;&amp;nbsp\;Dr. Mark Scott and Jim Leonard&lt;/p&gt;\n&lt;p&gt;
 &lt;strong&gt;Abstract:&lt;/strong&gt;&amp;nbsp\;Our project is dedicated to providing a h
 ybrid course on Programmable Logic Controllers (PLCs) for Miami University
  students. The course covers various topics regarding PLCs such as its his
 tory\, safety\, programming\, I/O\, etc. to allow students to be prepared 
 for PLC use in an industrial environment. Hands-on labs were also implemen
 ted to help students further develop the skills necessary for operation as
  well as gain practical experience with PLCs.&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;University o
 f Cincinnati&lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Project Title:&lt;/strong&gt;&amp;nbsp\;Ensuri
 ng UAS Airworthiness: Deep Learning Based Acoustic Health Monitoring of Mo
 tor Health&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Team Members:&lt;/strong&gt;&amp;nbsp\;Siddharth Urankar\
 , Prissha Chawla&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Faculty Advisor:&lt;/strong&gt;&amp;nbsp\;Dr. Manis
 h Kumar&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Abstract:&amp;nbsp\;&lt;/strong&gt;This work presents an inf
 light UAV powertrain health monitoring framework using machine learning fo
 r real time anomaly detection. We collected high fidelity acoustic signatu
 res from Brushless DC motors to develop a semi supervised 1D Convolutional
  Neural Network Autoencoder. By training exclusively on healthy acoustic p
 rofiles\, the system identifies mechanical degradation by analyzing recons
 truction error thresholds during flight. This non invasive approach suppor
 ts preflight checks and active monitoring within the In time Aviation Safe
 ty Management System (IASMS) to ensure airworthiness in mission critical e
 nvironments.&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Project Title:&lt;/strong&gt;&amp;nbsp\;UC Navvy&lt;/p&gt;\n&lt;
 p&gt;&lt;strong&gt;Team Members:&amp;nbsp\;&lt;/strong&gt;Elaine Mansour\, Justin Lin\, Karr 
 Stump&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Faculty Advisor:&lt;/strong&gt;&amp;nbsp\;Giovani Abuaitah&lt;/p&gt;
 \n&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt;&amp;nbsp\;UC-Navvy is an indoor and outdoor ca
 mpus navigation system designed specifically for the University of Cincinn
 ati. The system provided interactive map-based wayfinding across 46 campus
  buildings\, turn-by-turn routing instructions\, walk time estimates\, and
  a dedicated ADA-accessible routing mode that restricted paths to elevator
 - and ramp-equipped corridors. The application was built using React\, Typ
 eScript\, and MapLibre GL for the web platform\, and was wrapped in Capaci
 tor for native iOS and Android deployment\, delivering a unified cross-pla
 tform experience with no dependency on proprietary mapping SDKs. Routing w
 as powered by a custom Dijkstra implementation operating on a GeoJSON navi
 gation graph of the UC campus. The project addressed a gap left by mainstr
 eam tools\, namely the absence of ADA-aware campus routing and turn-by-tur
 n indoor/outdoor wayfinding for University of Cincinnati students\, facult
 y\, and visitors.&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;NKU&lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Title:&lt;/str
 ong&gt;&amp;nbsp\;Knowledge Distillation from Large Reasoning Models to Compact S
 tudent Language Models&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Team Members:&lt;/strong&gt;&amp;nbsp\; Gaura
 b Baral\, Aaditya Khanal&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Faculty Advisor:&lt;/strong&gt;&amp;nbsp\;D
 r. Yangyang Tao&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; This project explores k
 nowledge distillation from the large reasoning model DeepSeek-R1 to the co
 mpact Qwen2.5-7B using problems from the John O&amp;rsquo\;Bryan Mathematics C
 ompetition (2011&amp;ndash\;2025). A Chain-of-Thought dataset was generated th
 rough a dual-agent framework and used to fine-tune the student model via L
 oRA on Apple Silicon with the MLX framework. Early stopping at 200 iterati
 ons mitigated overfitting\, improving accuracy from 64.67% to 69.43%. The 
 fine-tuned model also demonstrated strong generalization\, achieving 73.1%
  accuracy on the MATH-500 benchmark. A key advantage is that the distilled
  Qwen2.5-7B model can be deployed on devices such as mobile phones or Rasp
 berry Pi systems\, enabling offline mathematical reasoning without interne
 t access.&lt;/p&gt;\n&lt;/div&gt;
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