[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120626-en":3,"doc-seo-120626-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120626,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","An Analysis of the Power Consumption of Various Computer Vision Machine Learning Techniques on a Microcontroller Utilizing the ClusterDuck Protocol","This senior project analyzes power consumption when running multiple computer-vision machine learning techniques on a microcontroller using the ClusterDuck protocol. The work covers engineering requirements, system design choices, and test plans, then documents development, integration, and measured outcomes across models and datasets. Results are presented through training metrics, model comparison tables, and power measurements over time for logistic regression, SVM, and CNN, enabling informed trade-offs for embedded deployment under constrained energy budgets.","AN ANALYSIS OF THE POWER CONSUMPTION OF VARIOUS COMPUTER VISION MACHINE LEARNING TECHNIQUES ON A MICROCONTROLLER UTILIZING THE CLUSTERDUCK  \nPROTOCOL  \nby  \nKaveh Shafiei  \n[kshafiei@calpoly.edu](kshafiei@calpoly.edu)  \nSenior Project  \nELECTRICAL ENGINEERING DEPARTMENT  \nCalifornia Polytechnic State University  \nSan Luis Obispo  \n13 June 2025  \nStatement of Disclaimer  \nSince this project is a result of a class assignment, it has been graded and accepted as fulfillment of the course requirements. Acceptance does not imply technical accuracy or reliability. Any use of information in this report is at the risk of the user. These risks may include catastrophic failure of the device or infringement of patent or copyright laws. California Polytechnic State University at San Luis Obispo and its staff cannot be held liable for any use or misuse of the project.  \nTABLE OF CONTENTS  \nSection Page Acknowledgements ............................................................................................................. 4  \nI. Introduction............................................................................................................. 7  \nII. Background............................................................................................................. 9  \nIII. Requirements .......................................................................................................... 17  \nIV. Design ..................................................................................................................... 20  \nV. Test Plans ................................................................................................................ 24  \nVI. Development and Construction ............................................................................. 26  \nVII. Integration and Test Results .................................................................................. 32  \nVIII. Conclusion .............................................................................................................. 42  \nIX. Bibliography ........................................................................................................... 44  \nAppendices  \nA. Analysis of Senior Project Appendix............................................................................ 48  \nB. Specifications.................................................................................................................. 54  \nC. Parts List and Costs ........................................................................................................ 55  \nD. Schedule and Time Estimates........................................................................................ 56  \nE. Code................................................................................................................................. 59  \nLIST OF TABLES AND FIGURES  \nTables Page 1. Table I: Engineering Specifications and Customer Requirements .............................. 17  \n2. Table II: Model Loss Functions ..................................................................................... 28  \n3. Table III: Uncompressed Model Training Parameters & Test Accuracy....................28  \n4. Table IV: Compressed Model Metrics........................................................................... 29  \n5. Table V: Logistic Regression Power Measurements.................................................... 30  \n6. Table VI: SVM Power Measurements........................................................................... 31  \n7. Table VII: CNN Power Measurements.......................................................................... 32  \n8. Table VIII: Model Power Consumption by Dataset ..................................................... 34  \n9. Table IX: Logistic Regression Power Consumption .................................................... 36  \n10. Table X: SVM Power Consumption.........................................................................","cbCaieKmWLcXtXbx","https://ap.wps.com/l/cbCaieKmWLcXtXbx","pdf",2138395,1,76,"English","en",105,"# Introduction\n# Background\n# Requirements\n# Design\n# Test Plans\n# Development and Construction\n# Integration and Test Results\n# Conclusion\n# Bibliography","[{\"question\":\"What is the main objective of the project?\",\"answer\":\"To measure and compare the power consumption of different computer-vision machine learning techniques when deployed on a microcontroller using the ClusterDuck protocol.\"},{\"question\":\"Which machine learning models are evaluated for power consumption?\",\"answer\":\"The project reports power measurements for logistic regression, SVM (support vector machine), and CNN (convolutional neural network).\"},{\"question\":\"How are the results organized and presented?\",\"answer\":\"Findings are presented using tables and figures that include model loss 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