E96 Final Project Codes

after SSH and SFTP into edison, moved files to github to publish.

FANN_system/Makefile

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+CC=gcc
+LDFLAGS=-lmraa -lfann
+CFLAGS=-Wall
+SOURCES=collect_neural_net_data.c examine_sensor_data.c test_neural_network.c train_neural_net.c
+EXECUTABLES=$(SOURCES:.c=)
+
+all: collect_neural_net_data examine_sensor_data test_neural_network train_neural_net
+
+collect_neural_net_data: collect_neural_net_data.c
+	$(CC) $(CFLAGS) -o collect_neural_net_data collect_neural_net_data.c $(LDFLAGS)
+
+examine_sensor_data: examine_sensor_data.c
+	$(CC) $(CFLAGS) -o examine_sensor_data examine_sensor_data.c $(LDFLAGS)
+
+test_neural_network: test_neural_network.c
+	$(CC) $(CFLAGS) -o test_neural_network test_neural_network.c $(LDFLAGS)
+
+train_neural_net: train_neural_net.c
+	$(CC) $(CFLAGS) -o train_neural_net train_neural_net.c $(LDFLAGS)
+
+clean: 
+	rm -f collect_neural_net_data examine_sensor_data test_neural_network train_neural_net
+	rm -f *~
+	rm -f TEST.net
+	rm -f test_data.txt

FANN_system/collect_neural_net_data.c

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+#include <stdio.h>
+#include <unistd.h>
+#include <mraa/aio.h>
+
+int main() 
+{
+
+	int i, j, success_flag;
+	int location;
+	int output[5];
+	int temp0, temp1, temp2;
+	float v0, v1, v2;
+	uint16_t value0, value1, value2;
+	mraa_aio_context lightsensor0, lightsensor1, lightsensor2;
+
+	lightsensor0 = mraa_aio_init(0);
+	lightsensor1 = mraa_aio_init(1);
+	lightsensor2 = mraa_aio_init(2);
+
+	// train data store to test_data.txt
+	FILE *fp;
+	fp = fopen("./test_data.txt", "wb");
+	fprintf(fp, "250\t3\t5\n");
+
+	// generate the test_data.txt output
+	for (location = 0; location < 5; location++) {
+		if (location != 4) {
+			printf("Please position the Test Object to cast a shadow on region (%d).\n", location);
+			printf("While you are casting this shadow and while the shadow is stable, please press the return key to capture data.\n");
+		} else {
+			printf("Please do not cast any shadow on the sensors and press the return key\n");
+		}
+
+		do {
+			success_flag = getchar();
+		} while (success_flag != '\n');
+
+		for (i = 0; i < 5; i++) {
+			output[i] = -1;
+		}
+		output[location] = 1;
+
+		// collect 50 data per area
+		for (j = 0; j < 50; j++) {
+
+			temp0 = 0;
+			temp1 = 0;
+			temp2 = 0;
+
+			// accurate the reading by using average of 50 readings
+			for (i = 0; i < 50; i++) {
+				temp0 += mraa_aio_read(lightsensor0);
+				temp1 += mraa_aio_read(lightsensor1);
+				temp2 += mraa_aio_read(lightsensor2);
+				usleep(5000);
+			}
+
+			value0 = temp0 / 50;
+			value1 = temp1 / 50;
+			value2 = temp2 / 50;
+
+			printf("%5d, %5d, %5d\n", value0, value1, value2);
+
+			// transfer raw data within in the range 0 - 1, easy for FANN to train
+			v0 = (float)value0 / 1000;
+			v1 = (float)value1 / 1000;
+			v2 = (float)value2 / 1000;
+
+			//write input, output to test_data.txt as training file format
+			fprintf(fp, "%f\t%f\t%f\n", v0, v1, v2);
+			fprintf(fp, "%d\t%d\t%d\t%d\t%d\n", output[0], output[1], output[2], output[3], output[4]);
+		}
+	}
+
+	// close everything
+	fclose(fp);
+
+	mraa_aio_close(lightsensor0);
+	mraa_aio_close(lightsensor1);
+	mraa_aio_close(lightsensor2);
+
+	return 0;
+}

FANN_system/collect_neural_net_data.c~

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+#include <stdio.h>
+#include <unistd.h>
+#include <mraa/aio.h>
+
+int main() 
+{
+
+	int i, j, success_flag;
+	int location;
+	int output[5];
+	int temp0, temp1, temp2;
+	float v0, v1, v2;
+	uint16_t value0, value1, value2;
+	mraa_aio_context lightsensor0, lightsensor1, lightsensor2;
+
+	lightsensor0 = mraa_aio_init(0);
+	lightsensor1 = mraa_aio_init(1);
+	lightsensor2 = mraa_aio_init(2);
+
+	// train data store to test_data.txt
+	FILE *fp;
+	fp = fopen("./test_data.txt", "wb");
+	fprintf(fp, "250\t3\t5\n");
+
+	// generate the test_data.txt output
+	for (location = 0; location < 5; location++) {
+		if (location != 4) {
+			printf("Please position the Test Object to cast a shadow on region (%d).\n", location);
+			printf("While you are casting this shadow and while the shadow is stable, please press the return key to capture data.\n");
+		} else {
+			printf("Please do not cast any shadow on the sensors and press the return key\n");
+		}
+
+		do {
+			success_flag = getchar();
+		} while (success_flag != '\n');
+
+		for (i = 0; i < 5; i++) {
+			output[i] = -1;
+		}
+		output[location] = 1;
+
+		// collect 50 data per area
+		for (j = 0; j < 50; j++) {
+
+			temp0 = 0;
+			temp1 = 0;
+			temp2 = 0;
+
+			// accurate the reading by using average of 50 readings
+			for (i = 0; i < 50; i++) {
+				temp0 += mraa_aio_read(lightsensor0);
+				temp1 += mraa_aio_read(lightsensor1);
+				temp2 += mraa_aio_read(lightsensor2);
+				usleep(5000);
+			}
+
+			value0 = temp0 / 50;
+			value1 = temp1 / 50;
+			value2 = temp2 / 50;
+
+			printf("%5d, %5d, %5d\n", value0, value1, value2);
+
+			// transfer raw data within in the range 0 - 1, easy for FANN to train
+			v0 = (float)value0 / 1000;
+			v1 = (float)value1 / 1000;
+			v2 = (float)value2 / 1000;
+
+			//write input, output to test_data.txt as training file format
+			fprintf(fp, "%f\t%f\t%f\n", v0, v1, v2);
+			fprintf(fp, "%d\t%d\t%d\t%d\t%d\n", output[0], output[1], output[2], output[3], output[4]);
+		}
+	}
+
+	// close everything
+	fclose(fp);
+
+	mraa_aio_close(lightsensor0);
+	mraa_aio_close(lightsensor1);
+	mraa_aio_close(lightsensor2);
+
+	return 0;
+}

FANN_system/examine_sensor_data.c

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+#include <stdio.h>
+#include <unistd.h>
+#include <mraa/aio.h>
+//this one gives us the avg sensor values so we have a good idea on how to scale it down to a value between 0-1
+int main() 
+{
+	int i;
+	int temp0, temp1, temp2;
+	uint16_t value0, value1, value2;
+	mraa_aio_context lightsensor0, lightsensor1, lightsensor2;
+//important to notice that the light sensors start off around 700 and should range from 100-500 at some point here
+	lightsensor0 = mraa_aio_init(0);
+	lightsensor1 = mraa_aio_init(1);
+	lightsensor2 = mraa_aio_init(2);
+
+	while(1) {
+
+		temp0 = 0;
+		temp1 = 0;
+		temp2 = 0;
+
+		for (i = 0; i < 50; i++) {
+			temp0 += mraa_aio_read(lightsensor0);
+			temp1 += mraa_aio_read(lightsensor1);
+			temp2 += mraa_aio_read(lightsensor2);
+			usleep(5000);
+		}
+
+		value0 = temp0 / 50;
+		value1 = temp1 / 50;
+		value2 = temp2 / 50;
+
+		printf("Light sensor 0: %5d, Light sensor 1: %5d, Light sensor 2: %5d\n", value0, value1, value2);
+
+		usleep(250000);
+	}
+
+	mraa_aio_close(lightsensor0);
+	mraa_aio_close(lightsensor1);
+	mraa_aio_close(lightsensor2);
+
+	return 0;
+}
+

FANN_system/examine_sensor_data.c~

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+#include <stdio.h>
+#include <unistd.h>
+#include <mraa/aio.h>
+//this one gives us the avg sensor values so we have a good idea on how to scale it down to a value between 0-1
+int main() 
+{
+	int i;
+	int temp0, temp1, temp2;
+	uint16_t value0, value1, value2;
+	mraa_aio_context lightsensor0, lightsensor1, lightsensor2;
+
+	lightsensor0 = mraa_aio_init(0);
+	lightsensor1 = mraa_aio_init(1);
+	lightsensor2 = mraa_aio_init(2);
+
+	while(1) {
+
+		temp0 = 0;
+		temp1 = 0;
+		temp2 = 0;
+
+		for (i = 0; i < 50; i++) {
+			temp0 += mraa_aio_read(lightsensor0);
+			temp1 += mraa_aio_read(lightsensor1);
+			temp2 += mraa_aio_read(lightsensor2);
+			usleep(5000);
+		}
+
+		value0 = temp0 / 50;
+		value1 = temp1 / 50;
+		value2 = temp2 / 50;
+
+		printf("Light sensor 0: %5d, Light sensor 1: %5d, Light sensor 2: %5d\n", value0, value1, value2);
+
+		usleep(250000);
+	}
+
+	mraa_aio_close(lightsensor0);
+	mraa_aio_close(lightsensor1);
+	mraa_aio_close(lightsensor2);
+
+	return 0;
+}
+

FANN_system/test_neural_network.c

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+#include <unistd.h>
+#include <mraa/aio.h>
+#include <stdio.h>
+#include "floatfann.h"
+//goal is to input 6 values , we have an array of 6 values as input
+//next step is to have an array output of 3 values, 1 for each thing we classify and ultimate goal is to see which one is closest to the correct classification of a certain type of motion.
+int main()
+{
+    int i;
+    int temp0, temp1, temp2,temp3, temp4, temp5, location;
+    uint16_t value0, value1, value2, value3, value4, value5;
+    float max; //
+    fann_type *calc_out;//object
+    fann_type input[6];//#of inputs 
+    struct fann *ann;
+    mraa_aio_context lightsensor0, lightsensor1, lightsensor2;
+
+    ann = fann_create_from_file("TEST.net");
+
+    lightsensor0 = mraa_aio_init(0);
+    lightsensor1 = mraa_aio_init(1);
+    lightsensor2 = mraa_aio_init(2);
+
+    while (1) {
+        temp0 = 0;
+        temp1 = 0;
+        temp2 = 0;
+        max = -100;
+
+        for (i = 0; i < 50; i++) {
+            temp0 += mraa_aio_read(lightsensor0);
+            temp1 += mraa_aio_read(lightsensor1);
+            temp2 += mraa_aio_read(lightsensor2);
+            usleep(10000);
+        }
+
+        value0 = temp0 / 50;
+        value1 = temp1 / 50;
+        value2 = temp2 / 50;
+
+        input[0] = (float) value0 / 1000;
+        input[1] = (float) value1 / 1000;
+        input[2] = (float) value2 / 1000;
+        calc_out = fann_run(ann, input);
+
+        for (i = 0; i < 5; i++) {
+            if (calc_out[i] > max) { //if the location is a specific input of brightness at this location, we "max" and say that is the location, for ever change in max. In other words, max is our way of identifying where our location is in the light sensor case, for the weight lifting, max will determine which type of motion we are closer to: curl or press.
+                max = calc_out[i];
+                location = i;
+            }
+        }
+//find max value for the light sensors
+	printf("Light sensor values: %d, %d, %d -> location is %d\n", value0, value1, value2, location);
+        sleep(1);
+    }
+ return 0;
+

FANN_system/test_neural_network.c~

@@ -0,0 +1,57 @@
+#include <unistd.h>
+#include <mraa/aio.h>
+#include <stdio.h>
+#include "floatfann.h"
+//goal is to input 6 values , we have an array of 6 values as input
+//next step is to have an array output of 3 values, 1 for each thing we classify and ultimate goal is to see which one is closest to the correct classification of a certain type of motion.
+int main()
+{
+    int i;
+    int temp0, temp1, temp2,temp3, temp4, temp5, location;
+    uint16_t value0, value1, value2, value3, value4, value5;
+    float max; //
+    fann_type *calc_out;//object
+    fann_type input[6];//#of inputs 
+    struct fann *ann;
+    mraa_aio_context lightsensor0, lightsensor1, lightsensor2;
+
+    ann = fann_create_from_file("TEST.net");
+
+    lightsensor0 = mraa_aio_init(0);
+    lightsensor1 = mraa_aio_init(1);
+    lightsensor2 = mraa_aio_init(2);
+
+    while (1) {
+        temp0 = 0;
+        temp1 = 0;
+        temp2 = 0;
+        max = -100;
+
+        for (i = 0; i < 50; i++) {
+            temp0 += mraa_aio_read(lightsensor0);
+            temp1 += mraa_aio_read(lightsensor1);
+            temp2 += mraa_aio_read(lightsensor2);
+            usleep(10000);
+        }
+
+        value0 = temp0 / 50;
+        value1 = temp1 / 50;
+        value2 = temp2 / 50;
+
+        input[0] = (float) value0 / 1000;
+        input[1] = (float) value1 / 1000;
+        input[2] = (float) value2 / 1000;
+        calc_out = fann_run(ann, input);
+
+        for (i = 0; i < 5; i++) {
+            if (calc_out[i] > max) { //if the location is a specific input of brightness at this location, we "max" and say that is the location, for ever change in max. In other words, max is our way of identifying where our location is in the light sensor case, for the weight lifting, max will determine which type of motion we are closer to: curl or press.
+                max = calc_out[i];
+                location = i;
+            }
+        }
+//find max value for the light sensors
+	printf("Light sensor values: %d, %d, %d -> location is %d\n", value0, value1, value2, location);
+        sleep(1);
+    }
+ return 0;
+