Air Quality Monitoring with Sipeed Longan Nano - RISC-V Gigadevice

#include <Arduino.h>

int MiCs = PA6;
int MQ5 = PA7;
int MQ7 = PB0;
int MQ3 = PB1;

int valMiCs = 0;
int valMQ5 = 0;
int valMQ7 = 0;
int valMQ3 = 0;

void setup() {
  Serial.begin(115200);
}

void loop() {
  valMiCs = analogRead(MiCs);
  valMQ5 = analogRead(MQ5);
  valMQ7 = analogRead(MQ7);
  valMQ3 = analogRead(MQ3);
  Serial.print(valMiCs);
  Serial.print(",");
  Serial.print(valMQ5);
  Serial.print(",");
  Serial.print(valMQ7);
  Serial.print(",");
  Serial.println(valMQ3);
}

#include <Arduino.h>
#include <Air_Quality_Monitoring_-_SIPEED_LONGAN_NANO_inferencing.h>
#define FREQUENCY_HZ        EI_CLASSIFIER_FREQUENCY
#define INTERVAL_MS         (1000 / (FREQUENCY_HZ + 1))

static unsigned long last_interval_ms = 0;
// to classify 1 frame of data you need EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE values
static float features[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE] = {};
// keep track of where we are in the feature array
size_t feature_ix = 0;

int MiCs = PA6;
int MQ5 = PA7;
int MQ7 = PB0;
int MQ3 = PB1;
static float valMiCs = 0;
static float valMQ5 = 0;
static float valMQ7 = 0;
static float valMQ3 = 0;
int InfValue = 0;

void setup() {
    pinMode(LED_BUILTIN,OUTPUT);
    Serial.begin(115200);
    Serial.println("Started");
}

void loop() {

    if (millis() > last_interval_ms + INTERVAL_MS) {
        last_interval_ms = millis();
        // read sensor data in exactly the same way as in the Data Forwarder example
        valMiCs = analogRead(MiCs);
        valMQ5 = analogRead(MQ5);
        valMQ7 = analogRead(MQ7);
        valMQ3 = analogRead(MQ3);
        Serial.print(valMiCs);
        Serial.print(",");
        Serial.print(valMQ5);
        Serial.print(",");
        Serial.print(valMQ7);
        Serial.print(",");
        Serial.println(valMQ3);
        Serial.print("\n");
        // fill the features buffer
        features[feature_ix++] = MiCs;
        features[feature_ix++] = MQ5;
        features[feature_ix++] = MQ7;
        features[feature_ix++] = MQ3;

        // features buffer full? then classify!
        if (feature_ix == EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE) {
            ei_impulse_result_t result = { 0 };

            // create signal from features frame
            signal_t signal;
            numpy::signal_from_buffer(features, EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE, &signal);

            // run classifier
            EI_IMPULSE_ERROR res = run_classifier(&signal, &result, false);
            ei_printf("run_classifier returned: %d\n", res);
            if (res != 0) return;

            // print predictions
            ei_printf("Predictions (DSP: %d ms., Classification: %d ms., Anomaly: %d ms.): \n",
                result.timing.dsp, result.timing.classification, result.timing.anomaly);

            // print the predictions
            for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++) {
                ei_printf("%s", result.classification[ix].label);
                InfValue = static_cast<int>(result.classification[ix].value*100);
                Serial.print(InfValue);
                Serial.print("\n");
                if(result.classification[ix].label == "Alcohol Leakage" && InfValue > 90){
                    digitalWrite(LED_BUILTIN,HIGH);
                    delay(2000);
                    digitalWrite(LED_BUILTIN,LOW);
                    delay(2000);
                };
            }
            // reset features frame
            feature_ix = 0;
        }
    }
}

void ei_printf(const char *format, ...) {
    static char print_buf[1024] = { 0 };

    va_list args;
    va_start(args, format);
    int r = vsnprintf(print_buf, sizeof(print_buf), format, args);
    va_end(args);

    if (r > 0) {
        Serial.write(print_buf);
    }
}