東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Machine Learning and Vision Using Edge Devices for Multimodal Chatbots and Bio-meteorological Sensing.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning and Vision Using Edge Devices for Multimodal Chatbots and Bio-meteorological Sensing./
作者:	Kulkarni, Karthik K.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	77 p.
附註:	Source: Masters Abstracts International, Volume: 83-02.
Contained By:	Masters Abstracts International83-02.
標題:	Computer engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28651722
ISBN:	9798535548982

Machine Learning and Vision Using Edge Devices for Multimodal Chatbots and Bio-meteorological Sensing.
Kulkarni, Karthik K.

Machine Learning and Vision Using Edge Devices for Multimodal Chatbots and Bio-meteorological Sensing. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 77 p.

Source: Masters Abstracts International, Volume: 83-02.

Thesis (M.Sc.)--Arizona State University, 2021.

This item must not be sold to any third party vendors.

Machine learning (ML) and deep learning (DL) has become an intrinsic part of multiple fields. The ability to solve complex problems makes machine learning a panacea. In the last few years, there has been an explosion of data generation, which has greatly improvised machine learning models. But this comes with a cost of high computation, which invariably increases power usage and cost of the hardware. In this thesis we explore applications of ML techniques, applied to two completely different fields - arts, media and theater and urban climate research using low-cost and low-powered edge devices.The multi-modal chatbot uses different machine learning techniques: natural language processing (NLP) and computer vision (CV) to understand inputs of the user and accordingly perform in the play and interact with the audience. This system is also equipped with other interactive hardware setups like movable LED systems, together they provide an experiential theatrical play tailored to each user. I will discuss how I used edge devices to achieve this AI system which has created a new genre in theatrical play. I will then discuss MaRTiny, which is an AI-based bio-meteorological system that calculates mean radiant temperature (MRT), which is an important parameter for urban climate research. It is also equipped with a vision system that performs different machine learning tasks like pedestrian and shade detection. The entire system costs around $200 which can potentially replace the existing setup worth $20,000. I will further discuss how I overcame the inaccuracies in MRT value caused by the system, using machine learning methods.These projects although belonging to two very different fields, are implemented using edge devices and use similar ML techniques. In this thesis I will detail out different techniques that are shared between these two projects and how they can be used in several other applications using edge devices.

ISBN: 9798535548982Subjects--Topical Terms:

621879
Computer engineering.
Subjects--Index Terms:

Computer vision

Machine Learning and Vision Using Edge Devices for Multimodal Chatbots and Bio-meteorological Sensing.
LDR:03208nmm a2200409 4500 001 2349092
005 20220920135736.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798535548982
035 $a (MiAaPQ)AAI28651722
035 $a AAI28651722
040 $a MiAaPQ $c MiAaPQ
100 1 $a Kulkarni, Karthik K. $3 3688488
245 1 0 $a Machine Learning and Vision Using Edge Devices for Multimodal Chatbots and Bio-meteorological Sensing.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 77 p.
500 $a Source: Masters Abstracts International, Volume: 83-02.
500 $a Advisor: Jayasuriya, Suren;Middel, Ariane.
502 $a Thesis (M.Sc.)--Arizona State University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Machine learning (ML) and deep learning (DL) has become an intrinsic part of multiple fields. The ability to solve complex problems makes machine learning a panacea. In the last few years, there has been an explosion of data generation, which has greatly improvised machine learning models. But this comes with a cost of high computation, which invariably increases power usage and cost of the hardware. In this thesis we explore applications of ML techniques, applied to two completely different fields - arts, media and theater and urban climate research using low-cost and low-powered edge devices.The multi-modal chatbot uses different machine learning techniques: natural language processing (NLP) and computer vision (CV) to understand inputs of the user and accordingly perform in the play and interact with the audience. This system is also equipped with other interactive hardware setups like movable LED systems, together they provide an experiential theatrical play tailored to each user. I will discuss how I used edge devices to achieve this AI system which has created a new genre in theatrical play. I will then discuss MaRTiny, which is an AI-based bio-meteorological system that calculates mean radiant temperature (MRT), which is an important parameter for urban climate research. It is also equipped with a vision system that performs different machine learning tasks like pedestrian and shade detection. The entire system costs around $200 which can potentially replace the existing setup worth $20,000. I will further discuss how I overcame the inaccuracies in MRT value caused by the system, using machine learning methods.These projects although belonging to two very different fields, are implemented using edge devices and use similar ML techniques. In this thesis I will detail out different techniques that are shared between these two projects and how they can be used in several other applications using edge devices.
590 $a School code: 0010.
650 4 $a Computer engineering. $3 621879
650 4 $a Electrical engineering. $3 649834
650 4 $a Computer science. $3 523869
650 4 $a Language. $3 643551
650 4 $a Software. $2 gtt. $3 619355
650 4 $a Deep learning. $3 3554982
650 4 $a Application programming interface. $3 3562904
650 4 $a Protocol. $3 3564384
650 4 $a Vision systems. $3 3685322
650 4 $a Verbal communication. $3 3560678
650 4 $a Artificial intelligence. $3 516317
650 4 $a Mathematical functions. $3 3564295
650 4 $a Internet of Things. $3 3538511
650 4 $a Clustering. $3 3559215
650 4 $a Natural language. $3 3562052
650 4 $a Computers. $3 544777
650 4 $a Data collection. $3 3561708
650 4 $a Algorithms. $3 536374
650 4 $a Cloud computing. $3 1016782
653 $a Computer vision
653 $a Deep learning
653 $a Edge devices
653 $a Internet of things
653 $a Machine learning
653 $a Natural language processing
690 $a 0464
690 $a 0544
690 $a 0984
690 $a 0800
690 $a 0679
710 2 $a Arizona State University. $b Computer Engineering. $3 3289092
773 0 $t Masters Abstracts International $g 83-02.
790 $a 0010
791 $a M.Sc.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28651722