ADB - Arthur

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Michael Pan

Data Acquisition
Measurement & Calibration
Analysis

Visualize
Analyze
Data Logging / Mining
Forecasting

Environment
Air, Water, Toxins...
Location Awareness

Continuous Monitoring
Cloud Connected
Remote Diagnostics
Cloud Service EcoSystem

Any Device / Equipment
Any Time
Any Where

more projects = partners

SW/HW Engineers
Developers
Designers (Industrial & Visual)
Manufacturers
Sales & Distribution

Q &A

Arthur Kuo

ﬂuidﬁsh@gmail.com

0922 788 838

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Citiviz | Urban Intelligence High Performance Computing and Geo-Visualization for Decision Making Presented on June 18th 2013 in Lausanne (Switzerland) Alpine Venture Forum 2013 http://www.e-unlimited.com/events/view.aspx?events_pages_id=1672 About Citiviz: Citiviz creates a computing and geo-visualization ecosystem for urban intelligence, monitoring and decision making. Citiviz, founded in August 2012, is a spin-off from Ecole polytechnique fédérale de Lausanne - Geographic Information Systems Lab (http://lasig.epfl.ch). Citiviz creates a specialized urban intelligence ecosystem with unique products for strategic, tactical and operational decision making. We are delivering spatial algorithms, business intelligence and monitoring benefits for smart cities. Our solutions are used by multinationals and large enterprises, SMEs and large and small public institutions, professionals and experts, and academics and R&D groups. Citiviz’ ecosystem helps them in solving problems and in answering knowledge needs related to transportation, urban & regional planning, architecture and environmental engineering. Citiviz and international R&D partners have completed use cases within countries: Québec City, Barcelona, Genève, Portugal, Bagdad, Winterthur, Ljubljana, Nice Côte d'Azur. Using our proprietary algorithms and virtualized ecosystem, these use cases answer problems related to transportation (multi-modal accessibility, flux density analysis and exploration), to economic development management (density of economic activities), to urban planning (street network centrality, land uses, land pricing and building pricing based on hedonic modeling) and humanitarian crisis analysis (spatial-temporal crisis geo-visualization).

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The edge computing market today includes consumer apps and devices, and the industrial sector, where increasingly powerful CPUs drive everything from wind turbines to autonomous vehicles, robots, drones and equipment. The device market is growing explosively: These devices gather a wealth of data from a broad array of sensors – and have the potential to optimize efficiency, safety and performance, and revolutionize productivity and user experiences. But to deliver these benefits they need to become truly smart, performing analysis, training and inference on high volumes of sensor data on-the-fly. There is an urgent need for software that simplifies and automates data analysis and inference at the edge, helping devices and systems learn from and make predictions about their environment: Cameras that recognize and track their targets; self-driving cars that choose the least congested routes using real- time predictions for intersections ahead; and drones that dynamically swarm, find their targets and gather intelligence without human oversight. These examples require each device to make decisions based on a real-time analysis of its own sensor data fused with the analysis and predictions from other systems: Drones in a swarm need to collaborate or they will collide; they must gossip their insights to each other to enable the swarm to perform effectively. Today, the software to enable each of these complex scenarios must be developed from scratch, starting with raw data feeds and network protocols. To unlock the potential of an edge environment rich in sensors and power-efficient computing platforms developers need a simple way to get from vast amounts of raw data to insights and predictions. What's needed is a new Architecture for the intelligent edge – one that consumes raw data from devices at the edge, and automatically creates a “digital twin” for each real-world system from its data. Digital twins statefully process their own data at the edge, analyzing, learning and predicting in real-time. Digital twins can find anomalies or correlations in their own data, and self-train powerful neural network models that enable them to predict their future performance, then share semantically enriched insights with other digital twins to solve system problems. The architecture helps application developers by dynamically creating digital twins that learn from their own data – automatically building a model of the real world that is always up to date, executes in real-time, and makes accurate predictions of the behavior of complex systems.

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ADB - Arthur

1. ADB & APPLICATIONS 郭岱碩 (Arthur)

2. 1981

3. 2007

4. Data Acquisition Measurement & Calibration Analysis

5. Portability ?

6. Visualize Analyze Data Logging / Mining Forecasting

9. Personal Devices

10. Real Time Location Aware Cloud

11. Environment Air, Water, Toxins... Location Awareness

12. Onsite Maintainence Field Diagnostics

13. Continuous Monitoring Cloud Connected Remote Diagnostics Cloud Service EcoSystem

14.

15. Any Device / Equipment Any Time Any Where

16. Why ? WiFi ? Bluetooth ?

17. Easy Cost Factor

18.

19. have an idea...but... ?

20. USD 100,000,000

21. ADB where we are at ...

22. 16 x 14 mm Samples 2012-12

23. more projects = partners SW/HW Engineers Developers Designers (Industrial & Visual) Manufacturers Sales & Distribution

24. Q &A Arthur Kuo ﬂuidﬁsh@gmail.com 0922 788 838

ADB - Arthur

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