Allicdata Part #: | SOM-5788FG-U4A1E-ND |
Manufacturer Part#: |
SOM-5788FG-U4A1E |
Price: | $ 600.02 |
Product Category: | Embedded Computers |
Manufacturer: | Advantech Corp |
Short Description: | INTEL QM57 COM EXPRESS - I5-520E |
More Detail: | N/A |
DataSheet: | SOM-5788FG-U4A1E Datasheet/PDF |
Quantity: | 1000 |
Lead Free Status / RoHS Status: | Lead free / RoHS Compliant |
Moisture Sensitivity Level (MSL): | 1 (Unlimited) |
1 +: | $ 545.47300 |
Series: | -- |
Part Status: | Last Time Buy |
Lead Free Status / RoHS Status: | -- |
Core Processor: | Intel Core i5-520E |
Moisture Sensitivity Level (MSL): | -- |
Speed: | 2.4GHz |
Number of Cores: | -- |
Power (Watts): | -- |
Cooling Type: | Fan |
Size / Dimension: | 4.92" x 3.74" (125mm x 95mm) |
Form Factor: | COM Express Basic Module, Type II |
Expansion Site/Bus: | PCI, PCIe |
RAM Capacity/Installed: | 8GB/0GB |
Storage Interface: | SATA 2.0 (4), PATA |
Video Outputs: | DD, DP, DVI, HDMI, LVDS, VGA |
Ethernet: | 10/100/1000 Mbps |
USB: | USB 2.0 (8) |
Digital I/O Lines: | 8 |
Analog Input:Output: | -- |
Watchdog Timer: | Yes |
Operating Temperature: | 0°C ~ 60°C |
Due to market price fluctuations, if you need to purchase or consult the price. You can contact us or emial to us: sales@allicdata.com
Single Board Computers (SBCs), such as SOM-5788FG-U4A1E, are computers that are built on a single printed circuit board, which contains all components essential for the running of the computer. These systems are widely used for mission-critical applications, due to their high reliability, low cost, and low environmental footprint. They are also popular with hobbyists who wish to build their own computer systems.
The SOM-5788FG-U4A1E is an industrial-grade computing solution that contains integrated NXP hardware components and is configurable with Intel® OpenVINO™ toolkit. It provides an affordable compute platform for complex image processing and deep learning applications, and contains features such as an on-board SoC, industrial-grade Ethernet ports, and advanced Video Display Interface (VDI) ports. The integrated components of the SOM-5788FG-U4A1E allow it to be used in a variety of industrial applications, with its utmost performance.
SOM-5788FG-U4A1E’s application field includes vision technology, image recognition, navigations, smart factory, robotics, surveillance, industrial automation, and many more. It has the capability to process large amount of data in a fast working time and with the help of deep learning software, can detect anomalies and classify images accurately.
In terms of hardware features, the SOM-5788FG-U4A1E is equipped with multiple features to meet different requirements. The on-board SoC provides Quad ARM Cortex-A55 @1.8GHz as CPU cores, Plus GPU for graphics/machine learning processing. It also provides up to 8GB DDR4 SDRAM for large data storage. The SOM-5788FG-U4A1E also features industrial-grade Ethernet ports, which can support 10/100/1000 Mbps data rates. Furthermore, it has four USB 3.0 ports for universal communication and data exchange, two HDMI interfaces, and comes preinstalled with Linux operating system.
The SOM-5788FG-U4A1E works on the basis of a rule-based data processing function, combined with machine-learning algorithms. The different components of the system are configured in such a way to ensure that operations can be carried out in a reliable, error-free environment. For example, the Cortex-A55 CPU mentioned before uses a dynamic cache to ensure speedy performance, while the deep learning engine inside the system is used for complex image processing. Additionally, certain features such as the VDI interface allow the system to support various video formats.
In conclusion, the SOM-5788FG-U4A1E is a high-performance single board computer that has been designed for a wide range of industrial applications. It is equipped with a number of features such as an on-board SoC, industrial-grade Ethernet, and more, which allow it to be used for complex image recognition tasks. Additionally, it is capable of rule-based data processing, combined with machine-learning algorithms, which help it to accurately detect anomalies and classify images in real-time.
The specific data is subject to PDF, and the above content is for reference
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