Link Ads

More search type here...

August 5, 2023

Arduino coding for display LCD 16x2 using I2C wire

 Arduino code to display text on a 16x2 LCD using the I2C wire library and an I2C LCD module (PCF8574):




First, make sure you have installed the "LiquidCrystal_I2C" library. If you haven't, you can download it from the Library Manager in the Arduino IDE.

#include <Wire.h>
#include <LiquidCrystal_I2C.h>

// Set the LCD address (0x27 or 0x3F) according to your module
#define I2C_ADDR 0x27

// Set the LCD size (16x2)
#define LCD_COLUMNS 16
#define LCD_ROWS 2

// Create an instance of the LCD object
LiquidCrystal_I2C lcd(I2C_ADDR, LCD_COLUMNS, LCD_ROWS);

void setup() {
  // Initialize the LCD
  lcd.begin(LCD_COLUMNS, LCD_ROWS);

  // Turn on the backlight (if available)
  lcd.backlight();

  // Write text to the LCD
  lcd.print("Hello, World!");
}

void loop() {
  // Add your code here (if needed)
}


In this example, we use the "LiquidCrystal_I2C" library to simplify the communication between the Arduino and the I2C LCD module (PCF8574). The library provides functions like lcd.print(), lcd.setCursor(), lcd.clear(), and more to control the LCD easily.

Make sure you have connected the SDA and SCL pins of the I2C module to the corresponding pins on your Arduino. Also, don't forget to provide power and ground connections.

Before uploading the code to your Arduino, double-check the I2C address of your LCD module. If it's different from 0x27, update the line #define I2C_ADDR 0x27 with the correct address.

That's it! Now you should be able to see the "Hello, World!" message on your 16x2 LCD connected via I2C. Good luck with your project!

March 5, 2023

Color sensor calibration TCS 3200

Calibrating a color sensor like the TCS 3200 typically involves a few steps to ensure accurate color readings. Here's a general outline of the calibration process:

  1. Clean the sensor: Before calibrating the sensor, make sure it is free from dust and debris that could affect its readings. Use a clean cloth or compressed air to remove any contaminants from the sensor surface.
  2. Choose a calibration surface: Next, select a surface to use as your calibration target. Ideally, this surface should be a neutral color (e.g. white or gray) and have a matte finish to minimize reflections. You may also want to choose a surface that is representative of the types of colors you'll be measuring with the sensor.
  3. Measure the target: Place the calibration target under the sensor and measure its color using the sensor's built-in LEDs. Record the RGB values for the target color.
  4. Calculate correction factors: Compare the measured RGB values to the expected values for the target color, and calculate correction factors to adjust the sensor's readings. For example, if the sensor consistently reads red as slightly brighter than expected, you could apply a correction factor to decrease the reported red value.
  5. Apply correction factors: Once you have calculated correction factors for each color channel, apply them to the sensor's readings. You may need to adjust the correction factors over time as the sensor's performance changes or you encounter different lighting conditions.

Note that the exact calibration process may vary depending on your specific sensor and application. Be sure to consult the manufacturer's documentation for detailed instructions and recommended best practices.



To calibrate the TCS3200 color sensor, you can follow these steps:

  1. First of all, connect the TCS3200 sensor to the Arduino board and install the basic programming code which will read the colors from the sensor and display them on the serial monitor.
  2. Place the sensor on a white surface, then read the RGB color values on the serial monitor. Make sure the RGB value on white is around (255,255,255). If not, you can adjust the gain value on the sensor to optimize color readability.
  3. After setting the gain, place the sensor on a black surface and read the RGB color values on the serial monitor. Make sure the RGB value on black is around (0,0,0).
  4. After adjusting white and black, you can use other colors for reference such as red, green, blue or other colors. Make sure the resulting RGB value is as expected.
  5. Finally, you can adjust the integration time (INTEG) on the sensor to optimize color rendering. The longer the integration time, the more accurate the color reading results, but the slower the data processing speed. You can try to time the integration from 2,4,8,16, and 32 clock cycles.

Here is an example of basic program code to read RGB values on the TCS3200 color sensor:

#include <Adafruit_TCS3200.h>
Adafruit_TCS3200 colorSensor(TCS3200_S2, TCS3200_S3, TCS3200_OUT);
void setup() {
  Serial.begin(9600);
  colorSensor.begin();
}
void loop() {
  colorSensor.setResolution(TCS3200_18BIT);
  colorSensor.writeRegister(0x00, TCS3200_MEASUREMENT_MODE);
  delay(50);
   uint16_t red = colorSensor.readRed();
  uint16_t green = colorSensor.readGreen();
  uint16_t blue = colorSensor.readBlue();
  Serial.print("RGB: ");
  Serial.print(red);
  Serial.print(" ");
  Serial.print(green);
  Serial.print(" ");
  Serial.println(blue);
}

You can calibrate the sensor by changing the gain and integration time in the following sections:

colorSensor.setGain(TCS3200_GAIN_1X);
colorSensor.setIntegrationTime(TCS3200_INTEGRATIONTIME_2_4MS);

If the color reading results are still not accurate, you can try adjusting the gain and integration time until the resulting RGB values are as expected.



February 6, 2023

Arduino code for play vocal

This code uses the SoftwareSerial library to create a serial communication between the Arduino and a Bluetooth module. The Bluetooth module should be connected to the specified RX and TX pins and set to 9600 baud rate.

Note that this code is just a basic example and may need modifications depending on the specific setup and requirements.

#include <SoftwareSerial.h>
SoftwareSerial mySerial(10, 11); // RX, TX
void setup() {
  mySerial.begin(9600);
  pinMode(9, OUTPUT);
  digitalWrite(9, LOW);
}
void loop() {
  if (mySerial.available()) {
    digitalWrite(9, HIGH);
    mySerial.write(mySerial.read());
  }
}

 

 

February 2, 2023

IOT arduino logging data to web server

To log data from an Arduino IoT device to a web server, you can follow these general steps:

  1. Connect the Arduino to the internet, either through Wi-Fi or Ethernet.
  2. Use a library such as the HTTPClient library to make HTTP requests to the server.
  3. Write code to format the data you want to log into a JSON or CSV string.
  4. Send the data as a POST request to a server-side script (e.g. PHP, Python, Node.js) that can receive and store the data in a database.
  5. On the server side, use a database management system such as MySQL or MongoDB to store the data.
  6. Optionally, you can also retrieve the data from the database and display it on a web page.
Here is an example of sending data from an Arduino to a server:

#include <WiFi.h>
#include <HTTPClient.h>

const char* ssid     = "your_SSID";
const char* password = "your_PASSWORD";
const char* serverName = "your_SERVER_IP_ADDRESS";

void setup() {
  WiFi.begin(ssid, password);
  while (WiFi.status() != WL_CONNECTED) {
    delay(1000);
    Serial.println("Connecting to WiFi...");
  }
  Serial.println("Connected to WiFi");
}

void loop() {
  HTTPClient http;
  http.begin("http://" + String(serverName) + "/data");
  http.addHeader("Content-Type", "application/json");
  int httpResponseCode = http.POST("{\"value\":\"" + String(analogRead(A0)) + "\"}");
  String response = http.getString();
  Serial.println(httpResponseCode);
  Serial.println(response);
  http.end();
  delay(10000);
}

Here some example to temperature logging by LM35 temperature sensor:


Here is an example of how you could log temperature data from an LM35 temperature sensor connected to an Arduino to a web server:

#include <WiFi.h>
#include <HTTPClient.h>

const char* ssid     = "your_SSID";
const char* password = "your_PASSWORD";
const char* serverName = "your_SERVER_IP_ADDRESS";
const int temperaturePin = A0;

void setup() {
  WiFi.begin(ssid, password);
  while (WiFi.status() != WL_CONNECTED) {
    delay(1000);
    Serial.println("Connecting to WiFi...");
  }
  Serial.println("Connected to WiFi");
  pinMode(temperaturePin, INPUT);
}

void loop() {
  int reading = analogRead(temperaturePin);
  float voltage = reading * 5.0 / 1024;
  float temperature = (voltage - 0.5) * 100;

  HTTPClient http;
  http.begin("http://" + String(serverName) + "/data");
  http.addHeader("Content-Type", "application/json");
  int httpResponseCode = http.POST("{\"temperature\":\"" + String(temperature) + "\"}");
  String response = http.getString();
  Serial.println(httpResponseCode);
  Serial.println(response);
  http.end();
  delay(10000);
}

This code connects to a Wi-Fi network using the WiFi.begin method and waits until the connection is established. The pinMode method is used to set the pin connected to the LM35 to INPUT mode. In the loop function, the code reads the analog value from the LM35, converts it to a temperature value, and then sends a POST request to the server containing the temperature data in JSON format. The server's response is printed to the serial monitor for debugging purposes.


Here is an example of how you could log temperature data from an LM35 temperature sensor connected to an Arduino to the Blynk cloud platform:



#include <WiFi.h>
#include <BlynkSimpleEsp32.h>

char auth[] = "your_BLYNK_AUTH_TOKEN";
char ssid[] = "your_SSID";
char pass[] = "your_PASSWORD";

const int temperaturePin = A0;

void setup() {
  WiFi.begin(ssid, pass);
  while (WiFi.status() != WL_CONNECTED) {
    delay(1000);
    Serial.println("Connecting to WiFi...");
  }
  Serial.println("Connected to WiFi");
  Blynk.begin(auth, ssid, pass);
  pinMode(temperaturePin, INPUT);
}

void loop() {
  int reading = analogRead(temperaturePin);
  float voltage = reading * 5.0 / 1024;
  float temperature = (voltage - 0.5) * 100;

  Blynk.virtualWrite(V0, temperature);
  delay(1000);
}


This code connects to a Wi-Fi network using the WiFi.begin method and waits until the connection is established. The Blynk.begin method is used to initialize the Blynk library with the auth token and the Wi-Fi credentials. The pinMode method is used to set the pin connected to the LM35 to INPUT mode. In the loop function, the code reads the analog value from the LM35, converts it to a temperature value, and then sends the temperature data to Blynk's virtual pin 0 using the Blynk.virtualWrite method. The data can be displayed in a Blynk app using various widgets such as a gauge or a chart.






January 19, 2023

Arduino circuit for digital clock and Coding

An Arduino circuit for a digital clock can be created using an Arduino board, a 16x2 LCD display, a real-time clock module, and a few passive components such as resistors and a capacitor. The real-time clock module, such as the DS1307, provides the timekeeping functionality, while the LCD display is used to display the current time. The Arduino board is used to control the LCD and interface with the real-time clock module. A simple circuit diagram could be:


Arduino ------------------------- LCD
|
|
R1,R2
|
|
DS1307






Arduino oding for clock display
Here is an example of Arduino code for displaying the current time on an LCD using a real-time clock module:

Copy code
#include <Wire.h>
#include <LiquidCrystal_I2C.h>
#include <RTClib.h>

RTC_DS1307 rtc;
LiquidCrystal_I2C lcd(0x27, 16, 2);

void setup() {
  lcd.begin();
  lcd.backlight();
  rtc.begin();
  if (!rtc.isrunning()) {
    lcd.print("RTC is NOT running!");
    // following line sets the RTC to the date & time this sketch was compiled
    rtc.adjust(DateTime(F(__DATE__), F(__TIME__)));
  }
}

void loop() {
  DateTime now = rtc.now();
  lcd.setCursor(0, 0);
  lcd.print(now.hour(), DEC);
  lcd.print(':');
  lcd.print(now.minute(), DEC);
  lcd.print(':');
  lcd.print(now.second(), DEC);
  lcd.setCursor(0, 1);
  lcd.print(now.day(), DEC);
  lcd.print('/');
  lcd.print(now.month(), DEC);
  lcd.print('/');
  lcd.print(now.year(), DEC);
  delay(1000);
}
 

This code uses the Wire and RTClib libraries to interface with the real-time clock module. The LiquidCrystal_I2C library is used to control the LCD display. The code first checks if the real-time clock is running, and sets the time if it is not. Then, in the loop, it continuously updates the LCD display with the current time and date retrieved from the real-time clock.

Note: in the code above, you can see that the I2C address of the LCD is 0x27, if it's different for you, you need to change that to the correct one.






KALMAN FILTER IN ARDUINO without Header file kalman.h

This is a basic Kalman filter that uses a constant measurement noise and process noise. You can adjust these values to fine-tune the filter for your specific application. The filter takes a measurement (z_measured) and uses it to estimate the real value (z_real). The estimated value is then updated with each new measurement.

#define MEASUREMENT_NOISE 0.1
#define PROCESS_NOISE 0.05

double x_est_last = 0;
double P_last = 0;
double K;
double P;
double x_temp_est;
double x_est;
double z_measured = 0; // the measured value
double z_real = 0; // the real value

void KalmanFilter()
{
    x_temp_est = x_est_last;
    P = P_last + PROCESS_NOISE;
    K = P / (P + MEASUREMENT_NOISE);
    x_est = x_temp_est + K * (z_measured - x_temp_est);
    P = (1 - K) * P;
    x_est_last = x_est;
    P_last = P;
}

 


 

January 17, 2023

Arduino Code for Kalman Filter


The Kalman filter is a mathematical algorithm that uses a series of measurements observed over time, containing noise (random variations) and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone. It is used in a wide range of applications, including signal processing, control systems, and navigation.

The filter is named after its inventor, Rudolf Kalman, who first described it in a paper published in 1960. It is based on a series of mathematical equations that describe how the variables of interest (such as the position and velocity of an object) evolve over time, and how these variables are related to the measurements that are being made. The filter uses these equations to estimate the values of the variables based on the measurements, and then updates these estimates as new measurements become available.

The Kalman filter is particularly useful in systems where the variables of interest are subject to random variations (noise) and other inaccuracies, and where the measurements are also subject to similar errors. The filter is able to take into account the uncertainty in the measurements and the system dynamics, and produce estimates that are optimal in some sense (e.g., minimum variance).

Case Study

The LM35 is a precision temperature sensor that can be used to measure temperature in degrees Celsius. It can be used in conjunction with a Kalman filter to filter out noise and provide a more accurate temperature reading. Here is an example circuit for using an LM35 with a Kalman filter on an Arduino:

#include <Kalman.h>

Kalman kalman;
int lm35Pin = A0; // The pin the LM35 is connected to
double temperature;
double filteredTemperature;

void setup() {
  Serial.begin(9600);
  kalman.setMeasurementNoise(0.1); // Set the measurement noise variance
  kalman.setProcessNoise(0.01); // Set the process noise variance
}

void loop() {
  temperature = analogRead(lm35Pin) * 0.48828125; // Read the LM35 and convert to degrees Celsius
  filteredTemperature = kalman.updateEstimate(temperature); // Update the filtered temperature estimate
 

  Serial.print("Temperature: ");
  Serial.print(temperature);
  Serial.print("\tFiltered Temperature: ");
  Serial.println(filteredTemperature);
 
  delay(100);
}

In this example, the LM35 is connected to pin A0 on the Arduino, and the analogRead() function is used to read the voltage output of the LM35. The voltage is then converted to temperature in degrees Celsius using the LM35's voltage-to-temperature conversion factor.

The Kalman filter is used to filter out noise in the temperature measurement by updating the estimate of the temperature with the new measurement and the previous estimate.

You should adjust the measurement noise and process noise variance to match your system's characteristics and specific requirements.

It's important to note that the example code is for illustrative purposes only and may need to be modified to work with your specific hardware and application.


Here another Example Code

===========================================================

#include <Kalman.h>

Kalman kalmanX; // Create Kalman filter objects
Kalman kalmanY;

double x = 0; // Set initial values
double y = 0;
double dx, dy;

void setup() {
  Serial.begin(9600);

  // Set the measurement noise variance for each filter
  kalmanX.setMeasurementNoise(1);
  kalmanY.setMeasurementNoise(1);

  // Set the process noise variance for each filter
  kalmanX.setProcessNoise(0.01);
  kalmanY.setProcessNoise(0.01);
}

void loop() {
  x = kalmanX.updateEstimate(x + dx); // Update the estimate for x
  y = kalmanY.updateEstimate(y + dy); // Update the estimate for y

  Serial.print("X: ");
  Serial.print(x);
  Serial.print("\tY: ");
  Serial.println(y);

  delay(100);
}