EDGE COMPUTING ALGORITHM

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Edge Computing Algorithm
edge-computing
Reduce the time and distance of data processing to create maximum efficiency.
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Advantages of Our Products
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Official algorithm partner of international chip makers

Official algorithm partner of international chip makers

With R&D support resources and SDK of all the AI SoC Platform from Qualcomm, Ambarella, MTK, AXIS, Novatek and Realtek, ioNetworks can develop customized applications with high performance to create various AIOT devices.

Various AI algorithms

Various AI algorithms

Customized edge computing algorithms, over 45 recognitions, to various scenarios can be implemented in different AI SoC for multiple functions of AIoT devices.

High accuracy and performance

High accuracy and performance

Customized edge computing algorithms, over 45 recognitions, to various scenarios can be implemented in different AI SoC for multiple functions of AIoT devices.

Rich experience in cooperation

Rich experience in cooperation

ioNetworks’ AI models, deeply integrated with platforms of international chip makers, is adjustable to achieve higher accuracy in specific environment and various sites.

About the Edge Computing

Low Power, Low Bandwidth, Low Latency

Edge Computing processes, analyzes, and stores data directly at the data source; compared to central computing, it consumes only a few watts of energy and reduces the requirements of transmission bandwidth and the delay of time. In order to realize edge computing, ioNetworks’ AI Assessment and Deployment Kit (ADK) could be deployed into the System on a Chip (SoC), so that the computing nodes can be dispersed from the central side to the application side, and the inference can be performed on the device to avoid the delay caused by network transmission and to provide timely alerts and responses.

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AI Chip collaboration partners
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AI Chip collaboration partners
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邊緣運算 A.I. 演算法應用
AI Algorithm on Chip

ioNetworks can select the Edge Computing ADK (Assessment and Deployment Kit) to be embedded into the AI SoC system-on-chip and become an edge computing device according to different fields and needs, which allows the computing nodes to be deployed from the central side to the application side. The detection projects cover over 45 models, including people detection, people counting, loitering detection, object left detection, line crossing detection, camera tampering, vehicle detection algorithms supported for day and night, and more, suitable for various AI devices. Through the collection of site data, it provides real-time multivariate insights and predictive analysis.

LiDAR/ToF/Radar Application

Dash Camera

Healthcare
Device

Self-Driving
Vehicle

Locker

Mobile

Video Application

Home Camera

Webcam

Surveillance
Camera

Conference
Peripheral

Access Control

Metaverse Application

VR Peripheral

AR Peripheral

MR Peripheral

Devicem for
Medicare

Gaming Peripheral

邊緣運算 A.I. 演算法應用
AI Algorithm on Chip

ioNetworks can select the Edge Computing ADK (Assessment and Deployment Kit) to be embedded into the AI SoC system-on-chip and become an edge computing device according to different fields and needs, which allows the computing nodes to be deployed from the central side to the application side. The detection projects cover over 45 models, including people detection, people counting, loitering detection, object left detection, line crossing detection, camera tampering, vehicle detection algorithms supported for day and night, and more, suitable for various AI devices. Through the collection of site data, it provides real-time multivariate insights and predictive analysis.

LiDAR/ToF/Radar Application

Dash Camera

Healthcare Device

Self-Driving Vehicle

Locker

Mobile

Video Application

Home Camera

Webcam

Surveillance Camera

Conference Peripheral

Access Control

Metaverse Application

VR Peripheral

AR Peripheral

MR Peripheral

Devicem for Medicare

Gaming Peripheral

Advantages of our solution
+80% efficiency

↗ Event Trigger increase New Demand
↗ Enhance User Experience with AI features
↗ Shipment Growth with Affordable Price
↗ Security assessment and protection before data generation to improve data security.

-80% consumption

↙ Reduce Power Consumption (ESG)
↙ Reduce Cost (Bandwidth, storage, Cloud Computing)
↙ Reduce Lantency (only 50 ms to complete recognition)

Advantages of our solution
+80%

↗ Event Trigger increase New Demand
↗ Enhance User Experience with AI features
↗ Shipment Growth with Affordable Price
↗ Security assessment and protection before data generation to improve data security.

-80%

↙ Reduce Power Consumption (ESG)
↙ Reduce Cost (Bandwidth, storage, Cloud Computing)
↙ Reduce Lantency (only 50 ms to complete recognition)

Examples AI algorithms
Lane Detection

  • Powerfully Precise Lane Detection:
    Cutting-edge model development, powered by the Qualcomm AI accelerator, delivers efficient, low-power multi-lane and multi-object detection, processing over 50 images per second for real-time accuracy and timely decision-making.
  • Unwavering Accuracy:
    Boasting an impressive 96%+ accuracy rate, the model is trained on millions of image data points, encompassing both simulated and real-world scenarios. This ensures precise identification even in challenging conditions like nighttime or complex road environments.
  • Adaptable and Agile:
    Deep learning, motion vector prediction, edge detection, and other advanced techniques are employed to enhance the speed and accuracy of lane detection analysis. This adaptability makes the system suitable for diverse environments, from well-marked highways to winding rural roads.

  • Continuously Evolving for Safety:
    The model is constantly optimized and refined, staying at the forefront of lane detection technology. It empowers various safety features like lane departure warnings, active lane-keeping assistance, forward distance warnings, event notifications, and warning point prompts, enhancing driver safety and confidence.
  • Examples AI algorithms
    Lane Detection

  • Powerfully Precise Lane Detection:
    Cutting-edge model development, powered by the Qualcomm AI accelerator, delivers efficient, low-power multi-lane and multi-object detection, processing over 50 images per second for real-time accuracy and timely decision-making.
  • Unwavering Accuracy:
    Boasting an impressive 96%+ accuracy rate, the model is trained on millions of image data points, encompassing both simulated and real-world scenarios. This ensures precise identification even in challenging conditions like nighttime or complex road environments.
  • Adaptable and Agile:
    Deep learning, motion vector prediction, edge detection, and other advanced techniques are employed to enhance the speed and accuracy of lane detection analysis. This adaptability makes the system suitable for diverse environments, from well-marked highways to winding rural roads.

  • Continuously Evolving for Safety:
    The model is constantly optimized and refined, staying at the forefront of lane detection technology. It empowers various safety features like lane departure warnings, active lane-keeping assistance, forward distance warnings, event notifications, and warning point prompts, enhancing driver safety and confidence.
  • Examples applications
    AI Camera
    Product features

  • Recognition speed within 50 milliseconds
  • Embedded Linux 4.14
  • Ultra HD 8M megapixel CMOS image sensor
  • Simultaneous streaming of H.265 and H.264 encoded streams
  • Digital PTZ supported
  • HDR function up to 120dB
  • High efficiency IR LED, radiant distance up to 40m
  • Day & Night (IR cut removable)
  • ONVIF supported
  • Functions of Edge Computing

  • Traffic Flow Analytics:
    The analysis of vehicle type identification, traffic flow, and turning volume calculation is carried out for different usage scenarios. It also extends to the analysis of red-light detection and detention time to adjust the duration of red and green lights and road planning through the data results to solve congestion problems and provide a better driving experience for road users.
  • License Plate Recognition:
    License plates appearing on the screen can be correctly recognized while maintaining a high accuracy rate and recognition speed (recognition accuracy can reach over 95% in the daytime and over 85% at night within 0.2 seconds).
  • Violation detection:
    Integrated with the management system, it can detect road conditions 24 hours a day. The system will record the violation, location, time, and license plate information, providing the traffic bureau with a large amount of valid violation data. This allows them to make effective traffic planning and manpower arrangements for intersections that are prone to violations, and to reduce the time of traffic police officers.
  • Examples applications
    AI Camera
    Product features

  • Recognition speed within 50 milliseconds
  • Embedded Linux 4.14
  • Ultra HD 8M megapixel CMOS image sensor
  • Simultaneous streaming of H.265 and H.264 encoded streams
  • Digital PTZ supported
  • HDR function up to 120dB
  • High efficiency IR LED, radiant distance up to 40m
  • Day & Night (IR cut removable)
  • ONVIF supported
  • Functions of Edge Computing

  • Traffic Flow Analytics:
    The analysis of vehicle type identification, traffic flow, and turning volume calculation is carried out for different usage scenarios. It also extends to the analysis of red-light detection and detention time to adjust the duration of red and green lights and road planning through the data results to solve congestion problems and provide a better driving experience for road users.
  • License Plate Recognition:
    License plates appearing on the screen can be correctly recognized while maintaining a high accuracy rate and recognition speed (recognition accuracy can reach over 95% in the daytime and over 85% at night within 0.2 seconds).
  • Violation detection:
    Integrated with the management system, it can detect road conditions 24 hours a day. The system will record the violation, location, time, and license plate information, providing the traffic bureau with a large amount of valid violation data. This allows them to make effective traffic planning and manpower arrangements for intersections that are prone to violations, and to reduce the time of traffic police officers.
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