Embedded motherboards are typically referred to as CPU boards that are integrated into equipment for control and data processing, acting as the central processing unit of the device. Embedded devices have stringent requirements for size and power consumption, including heat dissipation from the embedded motherboard. The standard architecture of embedded systems encompasses two major processing systems: Reduced Instruction Set Computing (RISC) and Complex Instruction Set Computing (CISC).
In recent years, embedded technology has experienced rapid growth, leading to its extensive application in various industries. The characteristics of the embedded industry are quite prominent, with involvement in fields such as mobile phones, PDAs, car navigation, industrial control, military applications, and multimedia terminals. Embedded motherboards find their main application environments in sectors such as finance, petrochemicals, geological exploration, field-portable devices, environmental protection, military systems, power generation, railways, highways, aerospace, and subway systems.
Common types of embedded motherboards include 3.5-inch boards, PC104 boards, MINI-ITX, PICO-ITX, NANO-ITX, ETX, etc.
JWIPC embedded motherboard application sharing
The advancement of agriculture has led to an increasing demand for intelligent production equipment. In response to this, customers are utilizing fruit sorting systems that employ machine vision and AI algorithms to achieve precise classification and packaging of fruits. These systems leverage deep learning techniques to gather information such as fruit type, size, and color, thereby enhancing the value of agricultural products.
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he customer, a global leader in crystalline silicon solar cell production equipment manufacturing, aims to offer end-users a fully automated optical inspection machine for SMT production lines. They are seeking a machine vision control solution that is more suitable to their needs, with the objectives of cost reduction, efficiency improvement, and enhanced accuracy in crystal defect detection for end-users.