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Titlebook: Beginning STM32; Developing with Free Warren Gay Book 20181st edition Warren Gay 2018 STM32.STM32F103.STM32F103C8T6.ARM.ARM Cortex.Cortex-M

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51#
發(fā)表于 2025-3-30 11:21:07 | 只看該作者
52#
發(fā)表于 2025-3-30 15:26:29 | 只看該作者
OLED Using DMA,(direct memory access)?Organic light-emitting diode (OLED) controller, which can be exploited to perform the I/O operations and leave more cycles available for the CPU. This chapter will explore how to set up and use that DMA controller to drive the OLED device.
53#
發(fā)表于 2025-3-30 20:06:00 | 只看該作者
54#
發(fā)表于 2025-3-30 21:35:26 | 只看該作者
leading to advanced multi-tasking applications.Features libo.Using FreeRTOS and libopencm3 instead of the Arduino software environment, this book will help you develop multi-tasking applications that go beyond Arduino norms. In addition to the usual peripherals found in the typical Arduino device, t
55#
發(fā)表于 2025-3-31 02:15:24 | 只看該作者
56#
發(fā)表于 2025-3-31 05:17:32 | 只看該作者
CAN Bus, Bosch released the developed CAN protocol at the SAE Congress (Society of Automotive Engineers).. In 1991 the CAN 2.0 protocol was published by Bosch and was adopted in 1993 as the international standard (ISO 11898). Since then, automobiles have used the protocol for communication and to reduce wiring harness sizes by use of the bus.
57#
發(fā)表于 2025-3-31 10:07:36 | 只看該作者
Book 20181st editionArduino norms. In addition to the usual peripherals found in the typical Arduino device, the STM32 device includes a USB controller, RTC (Real Time Clock), DMA (Direct Memory Access controller), CAN bus and more.?.Each chapter contains clear explanations of the STM32 hardware capabilities to help ge
58#
發(fā)表于 2025-3-31 16:21:53 | 只看該作者
scheidung und den daraus resultierenden Konsequenzen für die involvierten Unternehmen — interpretiert eben vornehmlich als Erfolgswirkungen — verdeutlicht und insofern der aus ?konomischer Perspektive geforderten ganzheitlichen (strategisch orientierten) Betrachtung des Sachverhalts am n?chsten komm
59#
發(fā)表于 2025-3-31 21:22:55 | 只看該作者
60#
發(fā)表于 2025-4-1 00:20:20 | 只看該作者
Jayanti Nayak,Therese Keane,Kurt Seemannes. The developed method used three deep convolutional neural networks named DCNN1, DCNN2, and DCNN3, three deep convolutional neural networks combined with deep recurrent neural networks named DCNN-RNN1, DCNN-RNN2, and DCNN-RNN3, and two autoencoder neural networks named AE1 and AE2, to maximize th
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