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On Computational Imaging in the Era of Neural Sensing: The Sensor, the Data and the Algorithm
On Computational Imaging in the Era of Neural Sensing: The Sensor, the Data and the Algorithm
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211153126
- ISBN
- 9798346856023
- DDC
- 621.3
- 서명/저자
- On Computational Imaging in the Era of Neural Sensing: The Sensor, the Data and the Algorithm
- 발행사항
- [Sl] : University of California, Los Angeles, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 269 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
- 주기사항
- Advisor: Kadambi, Achuta.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2024.
- 초록/해제
- 요약In recent years, sensing and perception techniques have evolved to be heavily reliant on learning-based pipelines. There is a specific need to explore computational imaging (joint design of hardware and software) in the era of AI. This work bridges this gap by understanding what we term as "neural sensing" through three pillars: the sensor, the data, and the learning algorithm. In the context of contactless heart rate monitoring of humans using visual sensors and beyond, we show that each of these three pillars pose specific, critical problems with the current state of the art: equity across demographic groups, lack of scalable, diverse data, and low signal to noise ratio in sensor measurements inhibiting accurate vital sign monitoring. We explore each pillar with the aim of addressing these limitations and demonstrate how a fundamental understanding and treatment of each of this pillars is critical towards building an operational perception systems. Through this thesis, we make contributions towards understanding the various pillars of neural sensing for and beyond contactless heart rate sensing, while also advancing the state of the art in remote plethysmography.
- 일반주제명
- Electrical engineering
- 일반주제명
- Computer science
- 일반주제명
- Computer engineering
- 키워드
- Noise ratio
- 기타저자
- University of California, Los Angeles Electrical Engineering 0303
- 기본자료저록
- Dissertations Abstracts International. 86-06B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211153126
■006m o d
■007cr#unu||||||||
■020 ▼a9798346856023
■035 ▼a(MiAaPQ)AAI31765123
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621.3
■1001 ▼aChari, Pradyumna Venkatesh.
■24510▼aOn Computational Imaging in the Era of Neural Sensing: The Sensor, the Data and the Algorithm
■260 ▼a[Sl]▼bUniversity of California, Los Angeles▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a269 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-06, Section: B.
■500 ▼aAdvisor: Kadambi, Achuta.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2024.
■520 ▼aIn recent years, sensing and perception techniques have evolved to be heavily reliant on learning-based pipelines. There is a specific need to explore computational imaging (joint design of hardware and software) in the era of AI. This work bridges this gap by understanding what we term as "neural sensing" through three pillars: the sensor, the data, and the learning algorithm. In the context of contactless heart rate monitoring of humans using visual sensors and beyond, we show that each of these three pillars pose specific, critical problems with the current state of the art: equity across demographic groups, lack of scalable, diverse data, and low signal to noise ratio in sensor measurements inhibiting accurate vital sign monitoring. We explore each pillar with the aim of addressing these limitations and demonstrate how a fundamental understanding and treatment of each of this pillars is critical towards building an operational perception systems. Through this thesis, we make contributions towards understanding the various pillars of neural sensing for and beyond contactless heart rate sensing, while also advancing the state of the art in remote plethysmography.
■590 ▼aSchool code: 0031.
■650 4▼aElectrical engineering
■650 4▼aComputer science
■650 4▼aComputer engineering
■653 ▼aRemote plethysmography
■653 ▼aComputational imaging
■653 ▼aHeart rate monitoring
■653 ▼aNoise ratio
■653 ▼aLearning-based pipelines
■690 ▼a0544
■690 ▼a0984
■690 ▼a0464
■71020▼aUniversity of California, Los Angeles▼bElectrical Engineering 0303.
■7730 ▼tDissertations Abstracts International▼g86-06B.
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17165119▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
