optical encoder代码

下面是一个基本的光学编码器代码：

int PIN_A = 2;  // 此处输入A相信号引脚
int PIN_B = 3;  // 此处输入B相信号引脚

volatile int pos = 0; // 定义计数器变量

void setup() {
  pinMode(PIN_A, INPUT_PULLUP);  // 设置引脚为上拉输入模式
  pinMode(PIN_B, INPUT_PULLUP);  // 设置引脚为上拉输入模式

  attachInterrupt(digitalPinToInterrupt(PIN_A), updateEncoder, CHANGE);  // 将A相信号引脚与中断0绑定
}

void loop() {
  // 在这里写你的代码
}

void updateEncoder() {
  if (digitalRead(PIN_A) == digitalRead(PIN_B)) {
    pos++;
  } else {
    pos--;
  }
}

该代码使用两个引脚来接收光学编码器的A相和B相信号，并使用 `attachInterrupt()` 函数将A相信号与中断0绑定，以便在A相信号发生变化时更新计数器变量。

在 `updateEncoder()` 函数中，根据A相和B相信号的状态变化来更新计数器变量。如果A相和B相信号的状态相同，则计数器加1；如果状态不同，则计数器减1。

注意，在使用光学编码器时，需要注意引脚的连接方式和信号的电平，以确保正确的计数方向和计数值。

相关问题

transformer encoder 代码

以下是一个示例的Transformer Encoder的代码：

import torch
import torch.nn as nn

class TransformerEncoder(nn.Module):
    def __init__(self, input_dim, hidden_dim, num_layers, num_heads):
        super(TransformerEncoder, self).__init__()
        self.embedding = nn.Embedding(input_dim, hidden_dim)
        self.positional_encoding = PositionalEncoding(hidden_dim)
        self.encoder_layers = nn.ModuleList([
            TransformerEncoderLayer(hidden_dim, num_heads) for _ in range(num_layers)
        ])
        
    def forward(self, input):
        embedded_input = self.embedding(input)
        encoded_input = self.positional_encoding(embedded_input)
        
        for encoder_layer in self.encoder_layers:
            encoded_input = encoder_layer(encoded_input)
        
        return encoded_input

class PositionalEncoding(nn.Module):
    def __init__(self, hidden_dim, max_length=1000):
        super(PositionalEncoding, self).__init__()
        self.hidden_dim = hidden_dim
        self.max_length = max_length
        self.positional_encoding = self.generate_positional_encoding()
        
    def forward(self, input):
        batch_size, seq_length, _ = input.size()
        positional_encoding = self.positional_encoding[:seq_length, :].unsqueeze(0).expand(batch_size, -1, -1)
        return input + positional_encoding
    
    def generate_positional_encoding(self):
        positional_encoding = torch.zeros(self.max_length, self.hidden_dim)
        position = torch.arange(0, self.max_length, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, self.hidden_dim, 2).float() * (-math.log(10000.0) / self.hidden_dim))
        positional_encoding[:, 0::2] = torch.sin(position * div_term)
        positional_encoding[:, 1::2] = torch.cos(position * div_term)
        return positional_encoding

class TransformerEncoderLayer(nn.Module):
    def __init__(self, hidden_dim, num_heads):
        super(TransformerEncoderLayer, self).__init__()
        self.multihead_attention = MultiheadAttention(hidden_dim, num_heads)
        self.feed_forward = FeedForward(hidden_dim)
        self.layer_norm1 = nn.LayerNorm(hidden_dim)
        self.layer_norm2 = nn.LayerNorm(hidden_dim)
        
    def forward(self, input):
        attention_output = self.multihead_attention(input)
        attention_output = self.layer_norm1(input + attention_output)
        
        feed_forward_output = self.feed_forward(attention_output)
        output = self.layer_norm2(attention_output + feed_forward_output)
        
        return output

class MultiheadAttention(nn.Module):
    def __init__(self, hidden_dim, num_heads):
        super(MultiheadAttention, self).__init__()
        self.hidden_dim = hidden_dim
        self.num_heads = num_heads
        self.head_dim = hidden_dim // num_heads
        
        self.query_projection = nn.Linear(hidden_dim, hidden_dim)
        self.key_projection = nn.Linear(hidden_dim, hidden_dim)
        self.value_projection = nn.Linear(hidden_dim, hidden_dim)
        self.output_projection = nn.Linear(hidden_dim, hidden_dim)
        
    def forward(self, input):
        batch_size, seq_length, _ = input.size()
        
        query = self.query_projection(input)
        key = self.key_projection(input)
        value = self.value_projection(input)
        
        query = self.split_heads(query)
        key = self.split_heads(key)
        value = self.split_heads(value)
        
        scaled_attention_scores = torch.matmul(query, key.transpose(-1, -2)) / math.sqrt(self.head_dim)
        attention_weights = nn.functional.softmax(scaled_attention_scores, dim=-1)
        
        attention_output = torch.matmul(attention_weights, value)
        attention_output = self.combine_heads(attention_output)
        
        output = self.output_projection(attention_output)
        return output
    
    def split_heads(self, input):
        batch_size, seq_length, hidden_dim = input.size()
        input = input.view(batch_size, seq_length, self.num_heads, self.head_dim)
        return input.transpose(1, 2)
    
    def combine_heads(self, input):
        batch_size, _, seq_length, hidden_dim = input.size()
        input = input.transpose(1, 2).contiguous()
        return input.view(batch_size, seq_length, self.num_heads * self.head_dim)

class FeedForward(nn.Module):
    def __init__(self, hidden_dim):
        super(FeedForward, self).__init__()
        self.hidden_dim = hidden_dim
        self.feed_forward = nn.Sequential(
            nn.Linear(hidden_dim, 4 * hidden_dim),
            nn.ReLU(),
            nn.Linear(4 * hidden_dim, hidden_dim)
        )
        
    def forward(self, input):
        return self.feed_forward(input)

51motor encoder测速代码

以下是使用51单片机测量电机编码器速度的示例代码：

#include <reg52.h>

#define uchar unsigned char
#define uint unsigned int

sbit IN1 = P1^0;        // 编码器A相输入口
sbit IN2 = P1^1;        // 编码器B相输入口
sbit ENCODER_LED = P1^2;    // 编码器LED指示灯

uint count = 0;         // 计数器变量
uint speed = 0;         // 速度变量
uchar time = 0;         // 定时器计数变量

void main()
{
    TMOD = 0x01;        // 定时器0工作在模式1：16位定时器
    TH0 = 0xFC;         // 定时器初值，1ms定时
    TL0 = 0x67;
    TR0 = 1;            // 启动定时器0
    ET0 = 1;            // 允许定时器0中断

    EA = 1;             // 全局中断使能

    while(1)
    {
        ENCODER_LED = 1;    // LED指示灯亮表示程序正常运行
    }
}

// 定时器0中断服务函数
void timer0() interrupt 1
{
    TH0 = 0xFC;         // 重新赋初值，1ms定时
    TL0 = 0x67;

    time++;             // 定时器计数变量加1

    if(time == 10)      // 定时器计数达到10ms
    {
        time = 0;       // 定时器计数变量清零

        if(IN1 == 1)    // 检测A相电平
        {
            if(IN2 == 0)    // 检测B相电平
            {
                count++;    // 正转计数器加1
            }
            else
            {
                count--;    // 反转计数器减1
            }
        }
        else
        {
            if(IN2 == 1)
            {
                count++;
            }
            else
            {
                count--;
            }
        }

        speed = count * 6;  // 速度计算公式，计数器乘以6得到转速
        count = 0;          // 计数器清零
    }
}

上述代码中，通过定时器0中断来周期性地读取编码器的A相和B相输入口电平状态，根据电平状态的变化来计算电机的转速。在本例中，定时器0的中断周期为1ms，每10ms计算一次电机的转速。需要注意的是，在编码器的A相和B相电平状态变化时，计数器的变化量需要根据电机的正反转方向来进行调整。