This document provides biographical information about Shih Huan and summarizes his academic and research experience. It outlines his educational background, including undergraduate and graduate studies. It also describes his military service and a research project on gait recognition during that time. Finally, it summarizes his dissertation research on compressed learning for time series classification.
16. 20Compressed sensing
► Most real-world signals are sparse in some basis
A𝑥 = 𝑦, A ∈ ℝ 𝑝×𝑛 𝑎𝑛𝑑 𝑝 ≪ 𝑛
► Dramatically reduce the transmission loading
a measure
17. 21Compressed sensing
Mostafa Mohsenvand Projects
► 𝑥 should be a 𝑘-sparse signal
» 1 to 1 relation between data and compressed domain
• A =
𝑟𝑎𝑛𝑑𝑛 𝑝,𝑛
𝑛
(mean=0, 𝜎 =
1
𝑛
)
19. 24Sparse representation
► Given 𝑫, envelope with size 𝒌
𝑬 𝒌 = 𝒁 𝒁 = 𝒛 𝟏, 𝒛 𝟐, … 𝒛 𝒏 , 𝒛𝒋 − 𝝁𝒋 ≤ 𝒌 ∙ 𝒔𝒕𝒅𝒋 , ∀ 𝒛𝒋 ∈ ℝ}
𝝁𝒋 = 𝒎𝒆𝒂𝒏(𝑻𝒋) , 𝒔𝒕𝒅𝒋 = 𝒔𝒕𝒅(𝑻𝒋)
► Profiling the time series dataset
20. 25Sparse representation
► Encoding time series T as a sparse series S
𝑠𝑗 = 1, 𝑖𝑓 𝑡𝑗 > 𝜇 𝑗 + 𝑘 ∙ 𝑠𝑡𝑑𝑗
𝑠𝑗 = −1, 𝑖𝑓 𝑡𝑗 < 𝜇 𝑗 − 𝑘 ∙ 𝑠𝑡𝑑𝑗
𝑠𝑗 = 0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
, 𝑓𝑜𝑟 𝑗 = 1 𝑡𝑜 𝑛
► Sparsity indicates similarity between a time series and 𝐷
24. Any questions?
You can find me at:
» shih0916@outlook.com
33THANKS!
http://www.freeimageslive.co.uk/
25. CREDITS
Special thanks to all the people who made and released these
awesome resources for free:
» Presentation template by SlidesCarnival
» Photographs by Unsplash
» Diverse device hand photos by Facebook Design Resources
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Editor's Notes
Outline
Personal profile & my research
Master degree in computer science
contact information
Personal experience
School days
Military life
開發此應用的靈感來自於學校舉辦的多場演講。演講中,偶爾總會有人忘記將手機鈴聲設定成震動或靜音,導致鈴聲大作的尷尬場面。在"如果能有一個APP能夠自動且準確地管理好手機提醒之模式的話,這個問題便能夠被解決"的想法下,我們決定製作Smart Ring APP。
本軟體的主要功能就是提供使用者個人化的手機模式設定(目前以來電通知為主,並可再加入飛航模式、藍芽、WIFI、3G行動網路、音樂\遊戲音量、螢幕亮度、螢幕休眠時間等。) ,主要分為兩個部份
Thesis topic
Compression scheme
Time series representation mehtod
Classification framework
Data collected continuously, time correlated series; communication & storage issue
Reduce dimension & recover with little loss
Extract info. From TS for ML model
The goal of compressed sensing is to provide measurement matrix A, with the number of measurements m as small as possible
M is the #sample, which is << Nyquist
Normal Random matrix A generated with specific parameters is usually good enough for most real world applications.
Exists in finance field
開盤價(opening)和收盤價(closing)
identify extreme overbought and oversold
Definition of envelope with size k: any time series Z in dataset D
The choice of k is critical
兩端分別是由開盤價和收盤價
identify extreme overbought and oversold
Propose a heuristic to make envelope distinguishable
k --- tradeoff between sparsity and distinguishability
X: size of envelope, Y: nonzero ratio of encoding result
Sparse property ⟹ transmission efficiency
Encode data from same class get more sparse result
With support of domain knowledge expert
Mainly focus on compressed represnetation
Concatenate all encoding result from each class
Keep good distinguishability with low compression ratio