語系:
繁體中文
English
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
Energy Models for Signal Processing ...
~
Meyer, Travis Robert.
FindBook
Google Book
Amazon
博客來
Energy Models for Signal Processing and Matrix Factorization.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Energy Models for Signal Processing and Matrix Factorization./
作者:
Meyer, Travis Robert.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:
122 p.
附註:
Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
Contained By:
Dissertation Abstracts International78-08B(E).
標題:
Applied mathematics. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10263301
ISBN:
9781369673296
Energy Models for Signal Processing and Matrix Factorization.
Meyer, Travis Robert.
Energy Models for Signal Processing and Matrix Factorization.
- Ann Arbor : ProQuest Dissertations & Theses, 2017 - 122 p.
Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
Thesis (Ph.D.)--University of California, Los Angeles, 2017.
In this work, we present a variety of energy-based methods that are solutions to problems in the fields of microscopy, hyperspectral and medical imaging, and data mining. These solutions are formulated from the perspective of extremization an energy function capturing deviation of the solution from observations and desirable properties. First we present new methods for improving imaging acquisition rates of atomic force microscopes. We propose and experimentally demonstrate image inpainting as a way to liberate scanner position limitations thereby enabling faster scans. Traditionally the scanner takes measurements in a raster pattern; in this work, we demonstrate that high-quality surface reproduction is attainable by sampling with non-raster patterns using variational image inpainting. With non-raster scan patterns existing thermomechanical drift error removal approaches no longer can be used. We propose a solution to this task with a highly effective corrective technique that utilize points of self-intersection. Our model only requires a few points of self-intersection that have minimal impact on scan time. Our correction model is potentially numerically unstable in some special, though easy to produce, cases. We propose a fitness based on analysis of the model energy that quantifies how well our method will perform for a given scan path. With minor experimental design modifications, often resulting simply from uncertainties in the scanner positioning, this fitness can be drastically increased and issues thereby alleviated. Due to its desirable properties, we focus specifically on improving the Archimedean spiral scan. By considering basic limitations of the scanner's tip speed and resonant frequency, we derive the parametrization that exactly obeys limitations while minimizing total scan time. With small and reasonable approximations the form of this scan becomes analytically simple to state and easy to implement in practice. We defend this optimal parameterization against other choices from the perspectives of scan time, scanner limitations, and sampling distribution uniformity.
ISBN: 9781369673296Subjects--Topical Terms:
2122814
Applied mathematics.
Energy Models for Signal Processing and Matrix Factorization.
LDR
:05519nmm a2200313 4500
001
2155377
005
20180426091045.5
008
190424s2017 ||||||||||||||||| ||eng d
020
$a
9781369673296
035
$a
(MiAaPQ)AAI10263301
035
$a
(MiAaPQ)ucla:15297
035
$a
AAI10263301
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
Meyer, Travis Robert.
$3
3343115
245
1 0
$a
Energy Models for Signal Processing and Matrix Factorization.
260
1
$a
Ann Arbor :
$b
ProQuest Dissertations & Theses,
$c
2017
300
$a
122 p.
500
$a
Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
500
$a
Adviser: Andrea L. Bertozzi.
502
$a
Thesis (Ph.D.)--University of California, Los Angeles, 2017.
520
$a
In this work, we present a variety of energy-based methods that are solutions to problems in the fields of microscopy, hyperspectral and medical imaging, and data mining. These solutions are formulated from the perspective of extremization an energy function capturing deviation of the solution from observations and desirable properties. First we present new methods for improving imaging acquisition rates of atomic force microscopes. We propose and experimentally demonstrate image inpainting as a way to liberate scanner position limitations thereby enabling faster scans. Traditionally the scanner takes measurements in a raster pattern; in this work, we demonstrate that high-quality surface reproduction is attainable by sampling with non-raster patterns using variational image inpainting. With non-raster scan patterns existing thermomechanical drift error removal approaches no longer can be used. We propose a solution to this task with a highly effective corrective technique that utilize points of self-intersection. Our model only requires a few points of self-intersection that have minimal impact on scan time. Our correction model is potentially numerically unstable in some special, though easy to produce, cases. We propose a fitness based on analysis of the model energy that quantifies how well our method will perform for a given scan path. With minor experimental design modifications, often resulting simply from uncertainties in the scanner positioning, this fitness can be drastically increased and issues thereby alleviated. Due to its desirable properties, we focus specifically on improving the Archimedean spiral scan. By considering basic limitations of the scanner's tip speed and resonant frequency, we derive the parametrization that exactly obeys limitations while minimizing total scan time. With small and reasonable approximations the form of this scan becomes analytically simple to state and easy to implement in practice. We defend this optimal parameterization against other choices from the perspectives of scan time, scanner limitations, and sampling distribution uniformity.
520
$a
In the area of medical imaging we address the issue of signal cleaning for simultaneous electroencephalographic and functional magnetic resonance imaging. During acquisition dominant signals are produced through the ballistocardiographic effects that have challenge variability over time. Noting some properties of the signals, we propose applying an existing model known as low-rank + sparse matrix decomposition. We performed experiments with twenty individuals in simultaneous capture to observe decreases in alpha-band neural activity following Gabor flashes and find that the proposed method improves signal cleaning results considerably when compared to an existing method known as independent component analysis. In the domain of hyperspectral unmixing we address the problem of unmixing with spectral variability. We propose and study using social sparsity to enforce sparsity assumptions in the context of existing models that extract per-material endmember bundles. In a trio of experiments, two quantitative and one qualitative, we demonstrate that social sparsity - in particular group lasso - improves the solution.
520
$a
In the final chapter of this work we investigate the recently popular machine learning problem of topic modeling. We present two models for solving this problem - latent Dirichlet allocation and non-negative matrix factorization - in their original forms, review the literature, and present what is known about the analytic relationship they share. In practice, because the problems are non-convex, the inference or optimization technique plays a role in solution quality. We therefore also summarize three popular algorithms for these models and frame the algorithms themselves in a common variational setting specific to the topic modeling problem. In addition to contributing this perspective for the models and algorithms together, we experimentally demonstrate differences in performance for the methods as well as practical topic model results. The final contribution of this work is two metrics for studying the distributional properties of topics extracted from documents with additional information e.g. time or location. We study these metrics with a geotagged Twitter data set taken from Madrid throughout 2011 and find that these simple metrics provide a useful summary for topics and can significantly simplify the initial process of studying topic model results when the number of topics is large.
590
$a
School code: 0031.
650
4
$a
Applied mathematics.
$3
2122814
690
$a
0364
710
2
$a
University of California, Los Angeles.
$b
Mathematics.
$3
2101079
773
0
$t
Dissertation Abstracts International
$g
78-08B(E).
790
$a
0031
791
$a
Ph.D.
792
$a
2017
793
$a
English
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10263301
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9354924
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入