CIICT 2009 Proceedings

More documents

Recommendations

Info

navigation. We define a walking area in 2D Euclidean spaceas an area where pedestrians can walk at random withoutusing fixed paths. A walking area is generally representedby a polygonal feature. Zheng [6] discussed data modelingof pedestrian networks including walking areas. In formerwork [8] we classified walking areas into three types withorthogonal attributes including the character of theirboundaries and entrances, concave-convex characteristics oftheir shape and the presence of impenetrable islands.Walking areas can be divided further into three subtypesderived from the access character of the boundaries andentrances: fixed entrances, open boundary (free entrance),and open boundary with restrictions. Considering thesubtypes of the other orthogonal factors in characterizingpolygon shapes there should be 12 general situations. Fordeveloping robust pedestrian navigation services the entireset of situations must be taken into account.Pan [7] put forward an algorithm generating optimalpaths within a polygon (a walking area) with interiorobstacles. This algorithm was based on Dijkstra’s algorithm.This work also provided some solutions for the specialbehaviors of pedestrians, such as preference for easywalking routes and preference for indoor routes.Zheng [8] describes a Two-Level Path PlanningAlgorithm for pedestrian navigation. In a related papercurrently under peer review the author describes the solutionfor pedestrian navigation with open boundary areas. Thesolution solves the problem to represent the link passingthrough open boundaries in any directions by building upthe connecting relationships between the open boundaryarea and other related spatial nodes (including simplifiedadjacent open boundary areas). At the first level this takesan open boundary area as one node of a link-node networkfor path planning outside walking areas. The second level isused for optimal path planning inside the walking areas. Thedetailed algorithm will be published in the near future.V. CONCLUSION AND FUTURE WORKOur goal is to obtain a cost effective way for datacollection and software deployment for LBS applications forthe case study of pedestrian navigation in a university town.A 2D data collection solution has been adopted based onOpenStreetMap. This paper has described our preliminarywork so far. Future work will focus on better optimal pathrepresentation for various topographic situations.Furthermore, we will develop test applications for mobileterminals to allow user testing to be performed. Figure 4shows a 3D data sample of NUIM campus obtained as partof the StratAG project. The extension of pedestriannavigation to 3D scenarios (for example, inside buildings)will also be investigated.ACKNOWLEDGMENTResearch presented in this paper was funded by aStrategic Research Cluster grant (07/SRC/I1168) by ScienceFoundation Ireland under the National Development Plan.The authors gratefully acknowledge this support.REFERENCES[1] Christoph Eckert, GPS Babel application, http://www.gpsbabel.org[2] Mordechai (Muki) Haklay and Patrick Weber, OpenStreetMap: User-Generated Street Maps, IEEE Pervasive Computing, 7, (4), 12-18,Oct.-Dec. 2008.[3] Immanuel Scholz, JOSM application, http://josm.openstreetmap.de/[4] Tim Waters, Map Wraper application, http://warper.geothings.net/[5] Christian Gaisbauer and Andrew U. Frank. Wayfinding Model forPedestrian Navigation, 11th AGILE International Conference onGeographic Information Science 2008.[6] Jianghua Zheng, Jianwei Tao, Jianli Ding, Abudukim Abuliz, andHanyu Xiang. Pedestrian Navigation Data Modelling for HybridTravel Patterns, Geoinformatics 2008. Proc. SPIE, 7144, 71442Y(2008)[7] Zheng Pan, Yuefeng Liu, Adam C. Winstanley, Lei Yan, JianghuaZheng. A 2-D ESPO Algorithm and Its Application In PedestrianPath Planning Considering Human Behavior, Proceedings of MUE’09(to be published in June 2009), IEEE CS.[8] Jianghua Zheng, Adam Winstanley, Zheng Pan, Seamus Coveney.Spatial Characteristics of Walking Areas for Pedestrian Navigation,Proceedings of MUE’09 (to be published in June 2009), IEEE CS.Figure 4 3D data sample22
Wiimote as a Navigation tool for PedestriansRicky Jacob, Adam Winstanley, Declan MeenaghComputer Science DepartmentNational University of Ireland MaynoothMaynooth, Kildare, Irelandrjacob@cs.nuim.ie, adam.winstanley@nuim.ieEoin Mac AoidhNational Centre for GeocomputationNational University of Ireland MaynoothMaynooth, Kildare, Irelandeoin.macaoidh@nuim.ieAbstract — Pedestrian Navigation requires effectivecommunication between the mobile device and the user. TheMobile device should be able to give feedback to the userensuring there is minimum input and attention of the userrequired. Mobile interaction for navigation has mostly beenthrough the use of visual interfaces with maps and annotations.This paper describes a Multi-modal, haptic interface with thevisual interface of an OpenStreetMap on a mobile platform. TheWiimote is used as the haptic tool which will vibrate based on thenavigation path to be taken by the user with signals from themobile application via a wireless connection using bluetooth.Keywords- Multi-Modal, Haptic, OpenStreetMap, Wiimote,Pedestrian navigationI. INTRODUCTIONAccording to a new study from ABI Research [5] the numberof subscribers to handset-hosted location based services (LBS)increased in 2008 to more than 18 million. Thus we can seean enormous growth during the coming years in the use ofmobile services. For good Location Based Services (LBS),there is always the need of an effective medium of interactionbetween the human and the mobile device. The various waysin which we can achieve that can roughly be classified underthe following: graphical user interfaces (GUI), speech userinterfaces, haptic user interfaces, gaze interfaces, andcomputer vision.When selecting an interaction technique, we must know whatis the task that we are looking to make easier and more naturalfor the user and how do we plan to do that decides upon whichmodalities we are going to use for input and output.Part of this is- graphic feedback, haptic feedback, auditory feedback,speech synthesis- manual (haptic) input, speech input, voice input, eyetracking, computer vision.If multimodal interaction is going to be used, then how do weovercome the complexities that come with it? The need forpsychological aspects is required inorder to assess which ofthe methods are easy and hard for understanding and whichpeople prefer using. We must select the programmingplatform and additional software packages based on themodalities we choose for our work. Lots of work to make thebrowser a location-aware one is going on and with theGeolocation API, the browser will now be able to recognizeyou current location and give you search results based on that.This will cut down on the human interaction part. Mozilla [6]has recently included a new experimental add-on called Geodeto their famous web browser - Firefox. Geode provides arudimentary implementation of geolocation for the currentversion of Firefox by using a single hard-coded locationprovider to enable Wifi-based positioning conforming to theW3C Geolocation specification [7] so that developers canbegin experimenting with enabling location-awareapplications today.Since mobile devices have issues such as small memory, smalldisplay, latency, and user input constraints, we need to ensurethe application does not overlook those factors. Also the needto choose between broswer based and widget basedapplication is of importance. The use of graphical display likemaps or texual description, requires the user to look into thescreen while he is on the move thus interupting the user’sother major work. Use of audio feedback also requires theusers attention at all times just limiting the user fromperforming other tasks at the same time.Car navigation systems have evolved well over the years toprovide better communication between the user and thesystem. These navigation systems work better with the GPSsatelliites as the cars travel on roads and it is quite easy to getgood signals from the satellites. Also in case of the car, theuser’s context does not vary also his field of view, thus it iscomparitively easier to provide effective navigation to theuser. The pedestrian navigation system is much morecomplex, as user context various quite rapidly and the systemshould dynamically change accordingly. Also it requires theuser’s attention if the system is a visual interface or audiofeedback. Haptics on the other hand will not need much of theuser’s attention when on the move. In this paper. we proposethe need for haptic feedback for pedestrian navigation alongwith the visual interface on the mobile device.II. NAVIGATION USING HAPTIC FEEDBACKThe word haptics refers to the capability to sense a natural orsynthetic mechanical environment through touch. Haptics alsoincludes kinesthesia (or proprioception), the ability to perceive23
Page 3 and 4: CIICT 2009Proceedings of the China-
Page 5 and 6: ForewordOn behalf of the organising
Page 7 and 8: TABLE OF CONTENTSSection 1A: ANTENN
Page 9 and 10: Section 3B: COMPUTER VISION 133A ne
Page 11 and 12: Section 1AANTENNAS AND CIRCUITS 11
Page 13 and 14: A block diagram of the platform is
Page 15 and 16: These two paths are averaged to red
Page 18 and 19: 4 mm -10 0 , -8 0 , -3 mm -8 0 , -6
Page 20 and 21: The results show that the separatio
Page 22 and 23: same size of annular-ring outer rad
Page 25 and 26: [4] C.Y.Sim, K.W.Lin, J.S.Row, Desi
Page 27 and 28: Interpretation of Spatial Movement
Page 30 and 31: III.DATA COLLECTIONAccurate spatial
Page 34 and 35: one’s body position, movement and
Page 36 and 37: world and will result in them being
Page 38 and 39: GPS/RFID/Self-contained Sensor Syst
Page 40 and 41: When the headway measures are equal
Page 42 and 43: [4] R.Liu, and S. Sinha, “Modelli
Page 44 and 45: Digital Audio Watermarking by Magni
Page 46 and 47: j 2 πβND = e(2)The frequency depe
Page 48 and 49: accurate in identifying the compone
Page 50 and 51: Furthermore, the decode experiment
Page 52 and 53: exists no spatial information of th
Page 54 and 55: iteration, one NMF for each frame.
Page 56 and 57: 7. CONCLUSIONSIn this paper we empl
Page 58 and 59: Dej 2 πβN= (1)The frequency depen
Page 60 and 61: length of x 1[n ], while it only ap
Page 62 and 63: Computing Modified Bessel functions
Page 64 and 65: From (9) both the numerator and den
Page 66 and 67: Section 2ARADIO SYSTEMS 156
Page 68 and 69: puncturing some parity RSDT columns
Page 70 and 71: [5] O'Droma M. S. and I. Ganchev.
Page 72 and 73: Administrations (CEPT) has proposed
Page 74 and 75: Figure 3 Proposed Test PlatformThe
Page 76 and 77: ACKNOWLEDGEMENTSThe authors wish to
Page 78 and 79: in an optimal fashion within that e
Page 80 and 81: whether the agent is in a learning
Page 82 and 83:
needs of the experiments as the goa
Page 84 and 85:
Section 2BGEOCOMPUTATION74
Page 86 and 87:
describes the proposal put forward
Page 88 and 89:
However, individual sensor measurem
Page 90 and 91:
Position Of Test Phone CallPosition
Page 92 and 93:
vol. 6, no. 3, pp. 30-38, 2007.[11]
Page 94 and 95:
environment’. The report states t
Page 96 and 97:
awareness communications about Twit
Page 98 and 99:
2.2 Flight route planningFlight rou
Page 100 and 101:
Spatial - temporal Simulation and P
Page 102 and 103:
and serious ones in turn. Overall,
Page 104 and 105:
[3] Cheng Weiming, Zhou Chenghu, an
Page 106 and 107:
(a) 1973 (b) 1990 (c) 2000Figure 1.
Page 108 and 109:
Effect of Hard RTOS on DPDC SCADA S
Page 110 and 111:
Therefore, this solution is not fea
Page 112 and 113:
already done on error free system o
Page 114 and 115:
8 PROPOSED REAL-TIME OPERATING SYST
Page 116 and 117:
HYBRID DECODING SCHEMES FOR TURBO-C
Page 118 and 119:
L a(u k ) SOVA/Log-MAPLog-MAP Inter
Page 120 and 121:
JavaScript code Fragment Analyzingf
Page 122 and 123:
The external JavaScript itself, whi
Page 124 and 125:
Section 3ACHANNELS AND PROPAGATION1
Page 126 and 127:
The DCF is assumed to have negligib
Page 128 and 129:
When m=6, the fluctuation have been
Page 130 and 131:
Across a 5 MHz spectrum, the maximu
Page 132 and 133:
Fig 6 shows the time variant charac
Page 134 and 135:
In the system, the RF signal is dir
Page 136 and 137:
of the different speeds UWB standar
Page 138 and 139:
3dB higher for the laser biased at
Page 140 and 141:
information for inter domain pre-au
Page 142 and 143:
In addition, the opendiameter imple
Page 144 and 145:
A new algorithm of edge detection b
Page 146 and 147:
Control structure elements of shape
Page 148 and 149:
3D TOOTH RECONSTRUCTIONWITH MULTIPL
Page 150 and 151:
3.1 Topological structure and base
Page 152 and 153:
Automatic Recognition of Head Movem
Page 154 and 155:
threshold model λ is created to ca
Page 156 and 157:
Dirt and Sparkle Detection for Film
Page 158 and 159:
[4] T. Komatsu, T. Saito, “Detect
Page 160 and 161:
Lightweight Signal Processing Algor
Page 162 and 163:
a two element PIR sensor, to obtain
Page 164 and 165:
the sensor node can readily be chec
Page 166 and 167:
parameters may not lead to simple a
Page 168 and 169:
0 (1 dR1)(1 dR2) 1[1]where d is t
Page 170 and 171:
epeatability is normally defined as
Page 172 and 173:
Integrated air quality monitoring:
Page 174 and 175:
colour coded data that are used to
Page 176 and 177:
Approximate Analysis of Fibre Delay
Page 178 and 179:
Virtual Traffic SourceA*f( t)= A* e
Page 180 and 181:
Section 4AANTENNAS AND CIRCUITS 217
Page 182 and 183:
Primary SpiralSecondary SpiralCoCbL
Page 184 and 185:
[4] A H Miklich, J X Przybysz, T J
Page 186 and 187:
ased on the ABCD transmission matri
Page 188 and 189:
RRe( Y3 _ LowFreq)=s(19)2 2 2Rs+ ω
Page 190 and 191:
Section 4BeLearning180
Page 192 and 193:
were investigated as important inpu
Page 194 and 195:
Time [min]1009080706050403020100Loc
Page 196 and 197:
Mean Rating for Different Encoding
Page 198 and 199:
Billing Issues when Accessing Perso
Page 200 and 201:
data consumed, for example 0.2€/M
Page 202 and 203:
and returns billing plans the learn
Page 204 and 205:
lecture size and the price per quan
Page 206 and 207:
Section 5ARADIO SYSTEMS 2196
Page 208 and 209:
and is uniformly distributed in the
Page 210 and 211:
When the number of users is 2, and
Page 212 and 213:
III.BLUETOOTH OPERATIONS IN SNIFF M
Page 214 and 215:
traffic or for POLL and NULL packet
Page 216 and 217:
Necessity for an Intelligent Bandwi
Page 218 and 219:
Spruce: Spruce has been one of the
Page 220 and 221:
Fig. 5 Comparison of bandwidth calc
Page 222 and 223:
Section 5BCOMPUTER NETWORKS 2212
Page 224 and 225:
2.1 CNFThe Content Navigating Funct
Page 226 and 227:
User BGF PD-FE CSCF CNF(AS) HSSPeer
Page 228 and 229:
5 ConclusionThis paper proposed an
Page 230 and 231:
d( X − X ) + ( Y −Y)= ∑ ∑2
Page 232 and 233:
Program Dependence Graph Generation
Page 234 and 235:
CDGPDGFigure 2. Program Dependence
Page 236 and 237:
Section 6ADIGITAL HOLOGRAPHY226
Page 238 and 239:
away. Immediately behind this plane
Page 240 and 241:
While this is not ideal as hair is
Page 242 and 243:
Figure 12. 3-D visualisation of 200
Page 244 and 245:
Speed up of Fresnel transforms for
Page 246 and 247:
[9]L. Onural and H. Ozaktas, "Signa
Page 248 and 249:
2009 China-Ireland International Co
Page 250 and 251:
2009 China-Ireland International Co
Page 252 and 253:
that enables the complete separatio
Page 254 and 255:
developed in the literature to remo
Page 256 and 257:
Section 6BINTELLIGENT SYSTEMS246
Page 258 and 259:
the vectors remain just form the in
Page 260 and 261:
d d x,y . If x and y don’t belon
Page 262 and 263:
Investigating the Influence of Popu
Page 264 and 265:
TABLE I.EXPERIMENTAL RESULTS FOR 26
Page 266 and 267:
Intelligent Learning Systems Where
Page 268 and 269:
FuzzyLogicBayesianReasoningClassifi
Page 270 and 271:
AN IMPROVED HAPTIC ALGORITHM FOR VI
Page 272 and 273:
Fig. 2. Proxy ImmersionFig. 1. Prox
Page 274 and 275:
1Corpus Design Techniques for Irish
show all

CIICT 2009 Proceedings

Create successful ePaper yourself

Delete template?

Save as template?