8-Pattern Recognition in Ubicom

8/6/2019 8-Pattern Recognition in Ubicom

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Pattern Recognition inUbiquitous Computing

Moongu Jeon

GIST


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Outline

Introduction to ubiquitous computing Mark Weiser The Computer for the Twenty-First Century"

Scientific American, pp. 94-10, September 1991.

Some Computer Science issues in UbiquitousComputing" Communications of the ACM, July1993.

Augmented reality Pattern recognition problems in Ubicomp

Introduction to Speech Recognition


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Trend of Technology

Development


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Ubiquitous Computing

Making computing an integral, invisible partof the way people live their lives, andavailable anytime anyplace.

Computers become parts of environment,and vanish into the background.

Integrating computers seamlessly into theworld.


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Ubiquitous in IT

Writing The first IT freed information from the limits of

individual memory

Books,magazines, newspapers, street signs,billboards, shop signs, candy wrappers whichare parts of the environment- ubiquitous

Current silicon-based IT

Huge number of computers and communicationdevices is far from having becoming of theenvironment


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Two Issues in Ubicomp

Location If a computer merely knows what room it is in,

it can adapt its behavior in significant ways

without requiring even a hint of artificialintelligence

Scale (size)

Tabs (inch-scale machine): Post-It notes

Pads (foot-scale): book or magazine

Boards (yard-scale): black (or bulletin) board


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Tabs

ParcTab Olivetti Cambridge

Research Lab activebadges Identify and keep track

of users or objects, anddo more tasks.

Roy Want, PARC tab

incorporating a smalldisplay Serves simultaneously

as an active badge,calendar, diary


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Pads

Scrap computer (analogous to scrappaper)

Can be grabbed and used anywhere.

Have no individual identity.


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Boards

Large and shared display Video screen

Bulletin boards Whiteboards

Electronic bookcase

LiveBoard (PARC)


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Other Issues Ubicomp

Cheap, low-power hardwarecomponents.

A network that ties them all together.

Software for screens and pens

Applications.

Privacy

Computational methods


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Augmented Reality

The opposite approach from virtual reality. VR encloses people in an artificial world using

computers.

AR augments objects in the real world using

computers.

Examples Digitaldesk (Wellner 1993)

KARMA (Feiner 1993) Flatland (Mynatt 1999) augmented whiteboard

UbiTV, MRWindow, ARTable (Woo 2006)


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Mobile AR

What is AR? To enhance the users

perception of and interactionwith the real world through

supplementing the real worldwith 3D virtual objects thatappear to coexist in thesame space as the realworld

We define AR system

Blends real and virtual, in areal environment

Interact on-the-fly Augment in 3D


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Mobile AR ()ubiTV

A

Invisible

C

B

ubiTV


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Mobile AR in U-Space


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Future of Ubicomp

A key concept of ubicomp is to usetechnology to create a calmerenvironment (Weiser 1998).

Computer technology should servehumans as environment that does not

occupy much of human attention,and should serve humans calmly notconsuming human effort.


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Pattern Recognition inUbicomp

Personalization Need to provide personalized service based on

each persons service history and preference.

Collection of user

s data using wireless sensors Recognition of users behavior pattern

Object or image recognition to get the

augmented data in U-space. Speech recognition


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Process of PatternRecognition

sensorsensor Featuregeneration

Featureselection

Classifierdesign

Systemevaluation

pattern


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Speech Recognition

Isolated word recognition (IWR) Continuous speech recognition (CSR)

Speaker-dependent recognition

Speaker-independent recognition

Dynamic time warping (DTW) - DP

finds an optimal match between two sequencesof feature vectors which allows for strechedand compressed sections of the sequence.


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Symmetrical DTW


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Dynamic Time Warping

Global constraints Local constraints

Monotonicity - matching paths cannot go

backwards in time End point constraints

Starts at (0,0) and ends at (,J) and whose first

transition is to the node (1,1) The cost for the transitions

Euclidean distance


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DTW Global Constraint


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DTW Local Constraints


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DTW Local Constraints

Sakoe and Chiba local constraints


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Test Words


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Feature Generation

Reference and test patterns 0.45, 0.4 sec. long

22050 Hz sampling rate

Frame size 512 samples long

100 samples overlapped

24, 21, 23 (Number of frames)


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Feature Selection

r(j), j=1,,J - reference pattern t(i), i=1,, - test pattern

xj(n), n=0,

511 -

samples for thejth frame of the reference pattern.

Taking DFT

Xi(m)=xi(n)exp(-j2mn/512)/5121/2,m=0,,511

Use the first 50 DFT coeff. as features

r(j)=[Xj(0) Xj(1) Xj(49)]T, j=1,J


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Cost

Euclidean distance between r(j) andt(i) corresponding to node (i,j) d(i,j)||r(j)-t(i)||

Symmetrical DTW D(i,j) = min[D(i-1,j-1),D(i-1,j),D(i,j-1)]

+d(i,j)

Asymmetrical DTW D(i,j) = min[D(i-1,j-2),D(i-1,j-1),D(I-

1,j)]+d(i,j)


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Results

For the test pattern love Dlove = 11.472 (0.221)

For the test pattern kiss

Dkiss = 25.155 (0.559)

Dlove < Dkiss - correct

8-Pattern Recognition in Ubicom

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