video analytics use cases in industrial iot · 2019-11-06 · video analytics use cases in...
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Founded to create real-time IIoT edge intelligence platform
Million in funding from industrial leaders$47.5
Commercial IIoT engagements globally100+
Analyst report coverage in 2018, including 7 from Gartner20+
Awards for innovation, edge intelligence, ML, AI and more 25+
Partners across OEM, cloud, SI, gateway, and semiconductors 50+
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Real-time edge intelligence for industrial IoT
Introducing FogHorn
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IIoT and AI Industry Recognition
1. Qualcomm2. Cisco3. Intel4. FogHorn Systems5. Amazon Web Services6. Microsoft7. Everythng8. Google9. Tesla10. IBM
10 Hot IoT Startups to Watch in 2018
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World’s smallest and fastest
inference engine that powers
transformational solutions
FogHorn Products
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EdgeAI Platform
Out of Box Solutions
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STREAMING ANALYTICS AT THE EDGE
A PRIMER
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DRIVERS FOR EDGE COMPUTING
Limited Connectivity
Bandwidth Costs
Risk of Cyberattacks
Real time decision making
High fidelity analytics
Traditional Batch AnalyticsCollect→ Store→ Transmit→ Process→ Transmit→ Act
FogHorn Streaming AnalyticsStream→ Process→ Act
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Batch vs. Streaming Analytics
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cloud
data center
edge device
Traditional Batch AnalyticsData at Rest
Streaming AnalyticsData in Motion
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Data at Rest vs. Data in Motion
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cloud
data center
edge device
Characteristics
Historic, several minutes to hours Data Freshness→ Real-time, milliseconds
Disk based and transmitted Data Location→ In memory and local
Often downsampled Data Resolution→ High resolution and raw
Seconds to hours Processing Speed→ Milliseconds to seconds
Periodic Processing Frequency→ Continual
High Power, Large Storage Processing Power→ Lower power, only memory
Historical analysis Use Cases→ Time critical decisions
IoT Data Requires Different AnalyticsHigh Volumes, Varieties and Velocities of Data
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Bandwidth costs of moving large volumes of data
Latency and real-timedecision making
Limited or noconnectivity
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Stream Processing Your Data with FogHorn
FogHorn’s Real-time Stream Processing Engine
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VEL® Complex Event Processing (CEP)
• Designed from the ground up in C++ for edge and small compute environments
• Performs real-time analysis of disparate asynchronous streams of sensor & control system data
• Executes complex pattern recognition and machine learning inferencing on high frequency and temporally continuous data
• Detects events, anomalies, and deviations from rules in real-time, enabling immediate control actions
• Highly advanced tooling for authoring, testing, debugging with live introspection and instrumentation of live system in production
TransportationWearables
Connected Cars
Connected Homes
Connected Cities
Industrial Internet
Manufacturing
Utilities
Oil & Gas
Healthcare
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The Rise of the Internet of Things
…IoT devices will grow to as many as 30 billion devices by 2020.McKinsey & Company. Image: Goldman Sachs.
Industries and Use Cases
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Manufacturing, Oil & Gas, Energy, Transportation, Smart Buildings
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EdgeML®
Industrial IoT Data Volume Overwhelming
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1 PB Mining
480 TB Jet engine
24 TB Automated manufacturing
1 TBLarge refinery
0.8 TB Large retail shop
0.5 TB US Smart meters
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IOT Signals Report 2019
Continuum of IoT Use Cases
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Edge Analytics
EdgeML®
Edge AI
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TRANSFORMER DEFECT DETECTION
Real-time edge testing improved yields and reduced scrap
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Transformer Defect Detection
Real-time edge testing at each stage of manufacture
for improved yields and reduced scrap
PRINT DEFECT DETECTION
Vision Model detectsvariations in printed characters and markings
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Transformer Defect DetectionPrint Defect Detection
FINISHED GOODS INSPECTION
Deep learning algorithms identify defects through vision models
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FACIAL RECOGNITION WITH DEEP LEARNINGDriver facial recognition as a vehicle access authentication mechanism
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Fusing of disparate sensor data streams
to derive insights
Pressure, vibration,
temperature, etc
Audio, video, 3D imaging
Geofencing for asset tracking, perimeter
monitoring
Sensor Fusion
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VIDEO-BASED SECURITY SURVEILLANCE
Real time monitoring & identifying worker movements in restricted zones
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HAZARD DETECTION USING VISION MODELSDetect hazardous Conditions in real time to ensure worker safety
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FLARE MONITORINGSOLUTION
Running deep learning models on Jetson Nano
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