sqr_cube.c

  name="google_ads_frame" marginwidth="0" marginheight="0" src="http://pagead2.googlesyndication.com/pagead/ads?client=ca-pub-5572165936844014&dt=1194442938015&lmt=1194190197&format=336x280_as&output=html&correlator=1194442937843&url=file%3A%2F%2F%2FC%3A%2FDocuments%2520and%2520Settings%2Flhh1%2F%E6%A1%8C%E9%9D%A2%2FCLanguage.htm&color_bg=FFFFFF&color_text=000000&color_link=000000&color_url=FFFFFF&color_border=FFFFFF&ad_type=text&ga_vid=583001034.1194442938&ga_sid=1194442938&ga_hid=1942779085&flash=9&u_h=768&u_w=1024&u_ah=740&u_aw=1024&u_cd=32&u_tz=480&u_java=true" frameborder="0" width="336" scrolling="no" height="280" allowtransparency="allowtransparency"> #include <stdio.h>

#define SQUARE(x) ((x) * (x))
#define CUBE(x) ((x) * (x) * (x))

void main(void)
 {
   printf("The square of 2 is %d/n", SQUARE(2));
   printf("The cube of 100 is %f/n", CUBE(100.0));
 }

 

#include "stm32f4xx_hal.h" // Device header #include "ADC.h" #include "stdio.h" ADC_HandleTypeDef hadc1; DMA_HandleTypeDef hdma_adc1; /* ADC1 init function */ void MX_ADC1_Init(void) { ADC_ChannelConfTypeDef sConfig = {0}; hadc1.Instance = ADC1; hadc1.Init.ClockPrescaler = ADC_CLOCK_SYNC_PCLK_DIV2; hadc1.Init.Resolution = ADC_RESOLUTION_12B; hadc1.Init.ScanConvMode = ENABLE; hadc1.Init.ContinuousConvMode = ENABLE; hadc1.Init.DiscontinuousConvMode = DISABLE; hadc1.Init.ExternalTrigConvEdge = ADC_EXTERNALTRIGCONVEDGE_NONE; hadc1.Init.ExternalTrigConv = ADC_SOFTWARE_START; hadc1.Init.DataAlign = ADC_DATAALIGN_RIGHT; hadc1.Init.NbrOfConversion = 3; hadc1.Init.DMAContinuousRequests = ENABLE; hadc1.Init.EOCSelection = ADC_EOC_SINGLE_CONV; if (HAL_ADC_Init(&hadc1) != HAL_OK) { printf("OERRRO1\r\n"); } /** Configure for the selected ADC regular channel its corresponding rank in the sequencer and its sample time. */ sConfig.Channel = ADC_CHANNEL_9; sConfig.Rank = 1; sConfig.SamplingTime = ADC_SAMPLETIME_144CYCLES; if (HAL_ADC_ConfigChannel(&hadc1, &sConfig) != HAL_OK) { printf("OERRRO2\r\n"); } sConfig.Channel = ADC_CHANNEL_10; sConfig.Rank = 2; sConfig.SamplingTime = ADC_SAMPLETIME_144CYCLES; if (HAL_ADC_ConfigChannel(&hadc1, &sConfig) != HAL_OK) { printf("OERRRO3\r\n"); } /** Configure for the selected ADC regular channel its corresponding rank in the sequencer and its sample time. */ sConfig.Channel = ADC_CHANNEL_15; sConfig.Rank = 3; sConfig.SamplingTime = ADC_SAMPLETIME_144CYCLES; if (HAL_ADC_ConfigChannel(&hadc1, &sConfig) != HAL_OK) { printf("OERRRO4\r\n"); } } void HAL_ADC_MspInit(ADC_HandleTypeDef* adcHandle) { GPIO_InitTypeDef GPIO_InitStruct = {0}; if(adcHandle->Instance==ADC1) { /* ADC1 clock enable */ __HAL_RCC_ADC1_CLK_ENABLE(); __HAL_RCC_GPIOC_CLK_ENABLE(); __HAL_RCC_GPIOB_CLK_ENABLE(); /**ADC1 GPIO Configuration PC5 ------> ADC1_IN15 PB1 ------> ADC1_IN9 */ GPIO_InitStruct.Pin = GPIO_PIN_5|GPIO_PIN_0; GPIO_InitStruct.Mode = GPIO_MODE_ANALOG; GPIO_InitStruct.Pull = GPIO_NOPULL; HAL_GPIO_Init(GPIOC, &GPIO_InitStruct); GPIO_InitStruct.Pin = GPIO_PIN_1; GPIO_InitStruct.Mode = GPIO_MODE_ANALOG; GPIO_InitStruct.Pull = GPIO_NOPULL; HAL_GPIO_Init(GPIOB, &GPIO_InitStruct); /* ADC1 DMA Init */ /* ADC1 Init */ hdma_adc1.Instance = DMA2_Stream0; hdma_adc1.Init.Channel = DMA_CHANNEL_0; hdma_adc1.Init.Direction = DMA_PERIPH_TO_MEMORY; hdma_adc1.Init.PeriphInc = DMA_PINC_DISABLE; hdma_adc1.Init.MemInc = DMA_MINC_ENABLE; hdma_adc1.Init.PeriphDataAlignment = DMA_PDATAALIGN_HALFWORD; hdma_adc1.Init.MemDataAlignment = DMA_MDATAALIGN_HALFWORD; hdma_adc1.Init.Mode = DMA_CIRCULAR; hdma_adc1.Init.Priority = DMA_PRIORITY_LOW; hdma_adc1.Init.FIFOMode = DMA_FIFOMODE_DISABLE; printf("HAL_ADC_MspInit\r\n"); if (HAL_DMA_Init(&hdma_adc1) != HAL_OK) { } __HAL_LINKDMA(adcHandle,DMA_Handle,hdma_adc1); /* USER CODE BEGIN ADC1_MspInit 1 */ /* USER CODE END ADC1_MspInit 1 */ } } /* USER CODE BEGIN 1 */ /* USER CODE END 1 */ 代码修改无法采集通道10和15的数值,采集出来等于0stm32f301 cubemx adc dma
06-10
内容概要:该论文聚焦于T2WI核磁共振图像超分辨率问题,提出了一种利用T1WI模态作为辅助信息的跨模态解决方案。其主要贡献包括:提出基于高频信息约束的网络框架,通过主干特征提取分支和高频结构先验建模分支结合Transformer模块和注意力机制有效重建高频细节;设计渐进式特征匹配融合框架,采用多阶段相似特征匹配算法提高匹配鲁棒性;引入模型量化技术降低推理资源需求。实验结果表明,该方法不仅提高了超分辨率性能,还保持了图像质量。 适合人群:从事医学图像处理、计算机视觉领域的研究人员和工程师,尤其是对核磁共振图像超分辨率感兴趣的学者和技术开发者。 使用场景及目标:①适用于需要提升T2WI核磁共振图像分辨率的应用场景;②目标是通过跨模态信息融合提高图像质量,解决传统单模态方法难以克服的高频细节丢失问题;③为临床诊断提供更高质量的影像资料,帮助医生更准确地识别病灶。 其他说明:论文不仅提供了详细的网络架构设计与实现代码,还深入探讨了跨模态噪声的本质、高频信息约束的实现方式以及渐进式特征匹配的具体过程。此外,作者还对模型进行了量化处理,使得该方法可以在资源受限环境下高效运行。阅读时应重点关注论文中提到的技术创新点及其背后的原理,理解如何通过跨模态信息融合提升图像重建效果。
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