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Arduino® UNO R4 Minima 開發板 (ABX00080)(義大利原廠) UNO R3 升級版
Introducing the Arduino UNO R4 Minima! This board boasts the RA4M1 microprocessor from Renesas, delivering increased processing power, expanded memory, and additional peripherals. And the best part? It stays true to the reliable UNO form factor and operates at a practical 5 V voltage. Brace yourself for an upgrade like no other with the Arduino UNO R4 Minima! 隆重推出 Arduino UNO R4 Minima！該板配備瑞薩電子 RA4M1 微處理器，可提供更高的處理能力、擴展的內存和更多外設。最好的部分是什麼？它忠實於可靠的 UNO 外形尺寸，並在實用的 5 V 電壓下運行。準備好迎接 Arduino UNO R4 Minima 獨一無二的升級吧！
《Here's what the UNO R4 Minima brings to the table》功能：
新的Arduino UNO R4有兩個版本UNO R4 WiFi和UNO R4 Minima。其採用Renesas RA4M1（Arm Cortex®-M4）運行，速度為48MHz，比UNO R3快3倍。 此外，SRAM從 R3 的2kB增加到32kB，閃存從32kB增加到256kB，以此來適應更複雜的項目。 此外，根據Arduino社區的要求，USB端口升級為USB-C，並且最大電源供應電壓增加到24V。該板提供了一個CAN總線，允許用戶通過連接多個擴展板來最小化佈線並執行不同的任務，最後，新板還包括一個12位模擬DAC。
UNO R4 Minima為那些尋求新的微控制器而沒有額外功能的人提供了一個划算的選擇。 在UNO R3成功的基礎上，UNO R4是所有人最好的原型和學習工具。在保留UNO系列已知的特性（標準外形係數、屏蔽兼容性、5V電壓、魯棒性）的同時，增加了新的功能。 由於其強大的設計和可靠的性能，UNO R4是對Arduino生態系統的一個有價值的補充。它適合初學者和有經驗的電子愛好者用於部署它們自己的項目。
Enhanced and improved, the Arduino UNO R4 Minima is armed with a powerful 32-bit microcontroller courtesy of Renesas. Brace yourself for increased processing power, expanded memory, and a whole new level of on-board peripherals. The best part? Compatibility with existing shields and accessories remains intact, and there's no need to make any changes to the standard form factor or 5 V operating voltage. 經過增強和改進，Arduino UNO R4 Minima 配備了瑞薩電子提供的功能強大的 32 位微控制器。準備好迎接增強的處理能力、擴展的內存和全新水平的板載外設。最好的部分？與現有屏蔽和配件的兼容性保持不變，並且無需對標準外形尺寸或 5 V 工作電壓進行任何更改。
Joining the Arduino ecosystem, the UNO R4 is a trusty addition suitable for both beginners and seasoned electronics enthusiasts. Whether you're just starting out or looking to push the boundaries of your projects, this robust board delivers reliable performance every time.
【Features】規格：
技術規格
Ｑ１：Can I use hardware compatible with the Arduino UNO R3 with the Arduino UNO R4 Minima? 我可以將與 Arduino UNO R3 兼容的硬件與 Arduino UNO R4 Minima一起使用嗎？ Ａ１：Yes, the Arduino UNO R4 Minima was specifically designed to ensure compatibility with previous shields and compatible hardware developed for the Arduino UNO R3. The UNO R4 Minima maintains the same mechanical and electrical compatibility, allowing you to seamlessly use your existing shields and hardware with the new board. This makes it easy to upgrade to the UNO R4 Minima without the need for significant changes or adaptations to your projects.
Ｑ２：Can I use my sketch developed for the UNO R3 in the UNO R4 Minima? 我可以在 UNO R4 Minima 中使用為 UNO R3 開發的草圖嗎？ Ａ２：Yes, if your sketch was developed using the Arduino API. In case you are using instructions only available for the AVR architecture, some changes need to be made to ensure compatibility.
Ｑ３：Are all libraries compatible with the UNO R3 also compatible with the UNO R4 Minima? 所有與 UNO R3 兼容的庫是否也與 UNO R4 Minima 兼容？ Ａ３：No, some UNO R3 libraries use instructions of the AVR architecture that are not compatible with the architecture of the UNO R4 Minima, however there are libraries that have already been ported as part of our early adopters program or are based on the Arduino API.
Based on the Renesas RA4M1 microcontroller, the new Arduino UNO R4 boasts 16x the RAM, 8x the flash, and a much faster CPU compared to the previous UNO R3. This means that unlike its predecessor, the R4 is capable of running machine learning at the edge to perform inferencing of incoming data. With this fact in mind, Roni Bandini wanted to leverage his UNO R4 Minima by training a model to predict the likelihood of a FIFA team winning their match. Bandini began his project by first downloading a dataset containing historical FIFA matches, including the country, team, opposing team, ranking, and neutral location. Next, the data was added to Edge impulse as a time-series dataset which feeds into a Keras classifier ML block and produces “win” and “lose/draw” values. Once trained, the model achieved an accuracy of 69% with a loss value of 0.58. Inputting the desired country and rank to make a prediction is done by making selections on a DFRobot LCD shield, and these values are then used to populate the input tensor for the model before it gets invoked and returns its classification results. Bandini's device demonstrates how much more powerful the Arduino UNO R4 is over the R3, and additional information on the project can be found here in his post.
