Chapter 14: Analog to Digital Conversion, Data Acquisition and Control

Jonathan Valvano and Ramesh Yerraballi


Throughout this course we have seen that an embedded system uses its input/output devices to interact with the external world. In this chapter we will focus on input devices that we use to gather information about the world. More specifically, we present a technique for the system to measure analog inputs using an analog to digital converter (ADC). We will use periodic interrupts to sample the ADC at a fixed rate. We will then combine sensors, the ADC, software, PWM output and motor interfaces to implement intelligent control on our robot car.

Learning Objectives:

  • Develop a means for a digital computer to sense its analog world.
  • Review digitization: Quantization, range, precision and resolution.
  • Extend the Nyquist Theorem to cases the ADC is used to sense information.
  • Study the basics of transducers: conversion of physical to electrical.
  • Use an optical sensor to measure distance to an object.


                     Video 14.0. Introduction to Digitization


14.1. Analog to Digital Conversion

An analog to digital converter (ADC) converts an analog signal into digital form, shown in Figure 14.1. An embedded system uses the ADC to collect information about the external world (data acquisition system.) The input signal is usually an analog voltage, and the output is a binary number. The ADC precision is the number of distinguishable ADC inputs (e.g., 4096 alternatives, 12 bits). The ADC range is the maximum and minimum ADC input (e.g., 0 to +3.3V). The ADC resolution is the smallest distinguishable change in input (e.g., 3.3V/4096, which is about 0.81 mV). The resolution is the change in input that causes the digital output to change by 1.

            Range(volts) = Precision(alternatives) Resolution(volts)

Figure 14.1. A 12-bit ADC converts 0 to 3.3V on its input into a digital number from 0 to 4095.


                     Video 14.1. Digitization Concepts


The most pervasive method for ADC conversion is the successive approximation technique, as illustrated in Figure 14.2. A 12-bit successive approximation ADC is clocked 12 times. At each clock another bit is determined, starting with the most significant bit. For each clock, the successive approximation hardware issues a new "guess" on Vdac by setting the bit under test to a "1". If Vdac is now higher than the unknown input, Vin, then the bit under test is cleared. If Vdac is less than Vin, then the bit under test is remains 1. In this description, bit is an unsigned integer that specifies the bit under test. For a 12-bit ADC, bit goes 2048, 1024, 512, 256,...,1. Dout is the ADC digital output, and Z is the binary input that is true if Vdac is greater than Vin.

Figure 14.2. A 12-bit successive approximation ADC.


                     Video 14.2. Successive Approximation

Interactive Tool 14.1

This tool allows you to go through the motions of a ADC sample capture using successive approximation. It is a game to demonstrate successive approximation. There is a secret number between 0 to 63 (6-bit ADC) that the computer has selected. Your job is to learn the secret number by making exactly 6 guesses. You can guess by entering numbers into the "Enter guess" field and clicking "Guess". The Tool will tell you if the number you guess is higher or lower than the secret number. When you have the answer, enter it into the "Final answer" field and click the "Submit answer" button.

The secret number is ???

The secret number is strictly less than these guesses             :
The secret number is greater than or equal to these guesses :


Observation: The speed of a successive approximation ADC relates linearly with its precision in bits.


Normally we don’t specify accuracy for just the ADC, but rather we give the accuracy of the entire system (including transducer, analog circuit, ADC and software). An ADC is monotonic if it has no missing codes as the analog input slowly rises. This means if the analog signal is a slowly rising voltage, then the digital output will hit all values one at a time, always going up, never going down. The figure of  merit of an ADC involves three factors: precision (number of bits), speed (how fast can we sample), and power (how much energy does it take to operate). How fast we can sample involves both the ADC conversion time (how long it takes to convert), and the bandwidth (what frequency components can be recognized by the ADC). The ADC cost is a function of the number and quality of internal components. Two 12-bit ADCs are built into the TM4C123/LM4F120 microcontroller. You will use ADC0 to collect data and we will use ADC1 and the PD3 pin to implement a voltmeter and oscilloscope.

14.2. ADC on the TM4C123/LM4F120

Table 14.1 shows the ADC0 register bits required to perform sampling on a single channel. There are two ADCs; you will use ADC0 and the grader uses ADC1. For more complex configurations refer to the specific data sheet. Bits 8 and 9 of the SYSCTL_RCGC0_R specify the maximum sampling rate, see Table 14.2. The TM4C123 can sample up to 1 million samples per second. Bits 8 and 9 of the SYSCTL_RCGC0_R specify how fast it COULD sample; the actual sampling rate is determined by the rate at which we trigger the ADC. In this chapter we will use software trigger mode, so the actual sampling rate is determined by the SysTick periodic interrupt rate; the SysTick ISR will take one ADC sample. On the TM4C123, we will need to set bits in the AMSEL register to activate the analog interface.
















































































































































Table 14.1. The TM4C ADC0 registers. Each register is 32 bits wide. You will use ADC0 and we will use ADC1 for the grader and to implement the oscilloscope feature.





1M samples/second


500K samples/second


250K samples/second


125K samples/second

Table 14.2. The ADC MAXADCSPD bits in the SYSCTL_RCGC0_R register.


Table 14.3 shows which I/O pins on the TM4C123 can be used for ADC analog input channels.










































































































































































Table 14.3. Twelve different pins on the LM4F/TM4C can be used to sample analog inputs.  You will use ADC0 and PE2 to sample analog input. If your PE2 pin is broken, you will have the option to perform Lab 14 with PE3 or PE5. We use ADC1 and PD3 to implement the oscilloscope feature.


The ADC has four sequencers, but you will use only sequencer 3 in Labs 14 and 15. We set the ADC0_SSPRI_R register to 0x0123 to make sequencer 3 the highest priority. Because we are using just one sequencer, we just need to make sure each sequencer has a unique priority. We set bits 15–12 (EM3) in the ADC0_EMUX_R register to specify how the ADC will be triggered. Table 14.4 shows the various ways to trigger an ADC conversion. More advanced ADC triggering techniques are presented in the book Embedded Systems: Real-Time Interfacing to ARM® Cortex™-M Microcontrollers. However in this course, we use software start (EM3=0x0). The software writes an 8 (SS3) to the ADC0_PSSI_R to initiate a conversion on sequencer 3. We can enable and disable the sequencers using the ADC0_ACTSS_R register. There are twelve ADC channels on the LM4F120/TM4C123. Which channel we sample is configured by writing to the ADC0_SSMUX3_R register. The mapping between channel number and the port pin is shown in Table 14.3. For example channel 9 is connected to the pin PE4. The ADC0_SSCTL3_R register specifies the mode of the ADC sample. We set TS0 to measure temperature and clear it to measure the analog voltage on the ADC input pin. We set IE0 so that the INR3 bit is set when the ADC conversion is complete, and clear it when no flags are needed. When using sequencer 3, there is only one sample, so END0 will always be set, signifying this sample is the end of the sequence. In this class, the sequence will be just one ADC conversion. We set the D0 bit to activate differential sampling, such as measuring the analog difference between two ADC pins. In our example, we clear D0 to sample a single-ended analog input. Because we set the IE0 bit, the INR3 flag in the ADC0_RIS_R register will be set when the ADC conversion is complete, We clear the INR3 bit by writing an 8 to the 8 to the ADC0_ISC_R register.






Software start


Analog Comparator 0


Analog Comparator 1


Analog Comparator 2


External (GPIO PB4)












Always (continuously sample)

Table 14.4. The ADC EM3, EM2, EM1, and EM0 bits in the ADC_EMUX_R register.


We perform the following steps to configure the ADC for software start on one channel. Program 14.1 shows a specific details for sampling PE4, which is channel 9. The function ADC0_InSeq3 will sample PE4 using software start and use busy-wait synchronization to wait for completion.

Step 1. We enable the port clock for the pin that we will be using for the ADC input.

Step 2. Make that pin an input by writing zero to the DIR register.

Step 3. Enable the alternative function on that pin by writing one to the AFSEL register.

Step 4. Disable the digital function on that pin by writing zero to the DEN register.

Step 5. Enable the analog function on that pin by writing one to the AMSEL register.

Step 6. We enable the ADC clock by setting bit 16 of the SYSCTL_RCGC0_R register.

Step 7. Bits 8 and 9 of the SYSCTL_RCGC0_R register specify the maximum sampling rate of the ADC. In this example, we will sample slower than 125 kHz, so the maximum sampling rate is set at 125 kHz. This will require less power and produce a longer sampling time, creating a more accurate conversion.

Step 8. We will set the priority of each of the four sequencers. In this case, we are using just one sequencer, so the priorities are irrelevant, except for the fact that no two sequencers should have the same priority.

Step 9. Before configuring the sequencer, we need to disable it. To disable sequencer 3, we write a 0 to bit 3 (ASEN3) in the ADC_ACTSS_R register. Disabling the sequencer during programming prevents erroneous execution if a trigger event were to occur during the configuration process.

Step 10. We configure the trigger event for the sample sequencer in the ADC_EMUX_R register. For this example, we write a 0000 to bits 15–12 (EM3) specifying software start mode for sequencer 3.

Step 11. Configure the corresponding input source in the ADCSSMUXn register. In this example, we write the channel number to bits 3–0 in the ADC_SSMUX3_R register. In this example, we sample channel 9, which is PE4.

Step 12. Configure the sample control bits in the corresponding nibble in the ADC0SSCTLn register. When programming the last nibble, ensure that the END bit is set. Failure to set the END bit causes unpredictable behavior. Sequencer 3 has only one sample, so we write a 0110 to the ADC_SSCTL3_R register. Bit 3 is the TS0 bit, which we clear because we are not measuring temperature. Bit 2 is the IE0 bit, which we set because we want to the RIS bit to be set when the sample is complete. Bit 1 is the END0 bit, which is set because this is the last (and only) sample in the sequence. Bit 0 is the D0 bit, which we clear because we do not wish to use differential mode.

Step 13. We enable the sample sequencer logic by writing a 1 to the corresponding ASENn. To enable sequencer 3, we write a 1 to bit 3 (ASEN3) in the ADC_ACTSS_R register.

void ADC0_InitSWTriggerSeq3_Ch9(void){ volatile unsigned long delay;

  SYSCTL_RCGC2_R |= 0x00000010;   // 1) activate clock for Port E

  delay = SYSCTL_RCGC2_R;         //    allow time for clock to stabilize

  GPIO_PORTE_DIR_R &= ~0x04;      // 2) make PE4 input

  GPIO_PORTE_AFSEL_R |= 0x04;     // 3) enable alternate function on PE2

  GPIO_PORTE_DEN_R &= ~0x04;      // 4) disable digital I/O on PE2

  GPIO_PORTE_AMSEL_R |= 0x04;     // 5) enable analog function on PE2

  SYSCTL_RCGC0_R |= 0x00010000;   // 6) activate ADC0

  delay = SYSCTL_RCGC2_R;        

  SYSCTL_RCGC0_R &= ~0x00000300;  // 7) configure for 125K

  ADC0_SSPRI_R = 0x0123;          // 8) Sequencer 3 is highest priority

  ADC0_ACTSS_R &= ~0x0008;        // 9) disable sample sequencer 3

  ADC0_EMUX_R &= ~0xF000;         // 10) seq3 is software trigger

  ADC0_SSMUX3_R &= ~0x000F;       // 11) clear SS3 field

  ADC0_SSMUX3_R += 9;             //    set channel Ain9 (PE4)

  ADC0_SSCTL3_R = 0x0006;         // 12) no TS0 D0, yes IE0 END0

  ADC0_ACTSS_R |= 0x0008;         // 13) enable sample sequencer 3


Program 14.1. Initialization of the ADC using software start and busy-wait (C14_ADCSWTrigger).


                     Video 14.3. ADC Initialization Ritual

Program 14.2 gives a function that performs an ADC conversion. There are four steps required to perform a software-start conversion. The range is 0 to 3.3V. If the analog input is 0, the digital output will be 0, and if the analog input is 3.3V, the digital output will be 4095.

            Digital Sample = (Analog Input (volts) 4095) / 3.3V(volts)

Step 1. The ADC is started using the software trigger. The channel to sample was specified earlier in the initialization.

Step 2. The function waits for the ADC to complete by polling the RIS register bit 3.

Step 3. The 12-bit digital sample is read out of sequencer 3.

Step 4. The RIS bit is cleared by writing to the ISC register.

Figure 14.3. The four steps of analog to digital conversion: 1) initiate conversion, 2) wait for the ADC to finish, 3) read the digital result, and 4) clear the completion flag.


// Busy-wait analog to digital conversion

// Input: none

// Output: 12-bit result of ADC conversion

unsigned long ADC0_InSeq3(void){  unsigned long result;

  ADC0_PSSI_R = 0x0008;            // 1) initiate SS3

  while((ADC0_RIS_R&0x08)==0){};   // 2) wait for conversion done

  result = ADC0_SSFIFO3_R&0xFFF;   // 3) read result

  ADC0_ISC_R = 0x0008;             // 4) acknowledge completion

  return result;


Program 14.2. ADC sampling using software start and busy-wait (C14_ADCSWTrigger).


                     Video 14.4. Capturing a Sample

There is software in the book Embedded Systems: Real-Time Interfacing to ARM® Cortex™-M Microcontrollers  showing you how to configure the ADC to sample a single channel at a periodic rate using a timer trigger. The most accurate sampling method is timer-triggered sampling (EM3=0x5).

: If the input voltage is 1.5V, what value will the TM4C 12-bit ADC return?

: If the input voltage is 0.5V, what value will the TM4C 12-bit ADC return?


14.3. Nyquist Theorem

To collect information from the external world into the computer we must convert it from analog into digital form. This conversion process is called sampling and because the output of the conversion is one digital number at one point in time, there must be a finite time in between conversions, Δt. If we use SysTick periodic interrupts, then this Δt is the time between SysTick interrupts. We define the sampling rate as

                                   fs = 1/Δt

If this information oscillates at frequency f, then according to the Nyquist Theorem, we must sample that signal at

                                   fs > 2f

Furthermore, the Nyquist Theorem states that if the signal is sampled with a frequency of fs, then the digital samples only contain frequency components from 0 to ½ fs. Conversely, if the analog signal does contain frequency components larger than ½ fs, then there will be an aliasing error during the sampling process (performed with a frequency of fs). Aliasing is when the digital signal appears to have a different frequency than the original analog signal.

Interactive Tool 14.2:

Discover the Nyquist Theorem. In this animation, you control the analog signal by dragging the handle on the left. Click and drag the handle up and down to create the analog wave (the blue continuous wave). The signal is sampled at a fixed rate (fs = 1Hz) (the red wave). The digital samples are connected by straight red lines so you can see the data as captured by the digital samples in the computer.

Exercise 1: If you move the handle up and down very slowly you will notice the digital representation captures the essence of the analog wave you have created by moving the handle. If you wiggle the handle at a rate slower than ½ fs, the Nyquist Theorem is satisfied and the digital samples faithfully capture the essence of the analog signal.

Exercise 2: However if you wiggle the handle quickly, you will observe the digital representation does not capture the analog wave. More specifically, if you wiggle the handle at a rate faster than ½ fs the Nyquist Theorem is violated causing the digital samples to be fundamentally different from the analog wave. Try wiggling the handle at a fast but constant rate, and you will notice the digital wave also wiggles but at an incorrect frequency. This incorrect frequency is called aliasing.

Figure 14.4 shows what happens when the Nyquist Theorem is violated. In both cases a signal was sampled at 2000 Hz (every 0.5 ms). In the first figure the 200 Hz signal is properly sampled, which means the digital samples accurately describe the analog signal. However, in the second figure, the 2200 Hz signal is not sampled properly, which means the digital samples do not accurately describe the analog signal. This error is called aliasing. Aliasing occurs when the input signal oscillates faster than the sampling rate and it characterized by the digital samples “looking like” it is oscillating at a different rate than the original analog signal. For these two sets of sampled data, notice the digital data are exactly the same.


Figure 14.4. Aliasing occurs when the input analog signal oscillates faster than the rate of the ADC sampling.


                     Video 14.5. Aliasing Demonstration: The Wagon Wheel Effect


Valvano Postulate: If fmax is the largest frequency component of the analog signal, then you must sample more than ten times fmax in order for the reconstructed digital samples to look like the original signal when plotted on a voltage versus time graph.


14.4. Data Acquisition and Control Systems

The measurand is a real world signal of interest like sound, distance, temperature, force, mass, pressure, flow, light and acceleration. Figure 14.5 shows the data flow graph for a data acquisition system or control system. The control system uses an actuator to drive a measurand in the real world to a desired value while the data acquisition system has no actuator because it simply measures the measurand in a nonintrusive manner.

Figure 14.5. Signal paths a data acquisition system.

The input or measurand is x. The output is y.  A transducer converts x into y. Examples include

·       Sound                           Microphone

·       Pressure, mass, force      Strain gauge, force sensitive resistor

·       Temperature                  Thermistor, thermocouple, integrated circuits

·       Distance                        Ultrasound, lasers, infrared light

·       Flow                             Doppler ultrasound, flow probe

·       Acceleration                   Accelerometer

·       Light                             Camera

·       Biopotentials                  Silver-Silver Chloride electrode


A wide variety of inexpensive sensors can be seen at

A nonmonotonic transducer is an input/output function that does not have a mathematical inverse. For example, if two or more input values yield the same output value, then the transducer is nonmonotonic. Software will have a difficult time correcting a nonmonotonic transducer. For example, the Sharp GP2Y0A21YK IR distance sensor has a transfer function as shown in Figure 14.6. If you read a transducer voltage of 2 V, you cannot tell if the object is 3 cm away or 12 cm away. However, if we assume the distance is always greater than 10cm, then this transducer can be used.


Figure 14.6. The Sharp IR distance sensor exhibits nonmonotonic behavior.


Details about transducers and actuators can be found in Embedded Systems: Real-Time Interfacing to ARM® Cortex™-M Microcontrollers, 2014, ISBN: 978-1463590154.

14.5. Robot Car Controller

The goal is to drive a robot car autonomously down a road. Autonomous driving is a difficult problem, and we have greatly simplified it and will use this simple problem to illustrate the components of a control system. Every control system has real-world parameters that it wishes to control. These parameters are called state variables. In our system we wish to drive down the middle of the road, so our state variables will be the distance to the left side of the road and the distance to the right side of the road as illustrated in Figure 14.7. When we are in the middle of the road these two distances will be equal. So, let’s define Error as:

      Error = DleftDright

If Error is zero we are in the middle of the road, so the controller will attempt to drive the Error parameter to zero.

Figure 14.7. Physical layout of the autonomous robot as is drives down the road.


                     Video 14.6a. IR Sensor for Robot Car


We will need sensors and a data acquisition system to measure Dleft and Dright. In order to simplify the problem we will place pieces of wood to create walls along both sides of the road, and make the road the same width at all places along the track. The Sharp GP2Y0A21YK0F infrared object detector can measure distance ( from the robot to the wood. This sensor creates a continuous analog voltage between 0 and +3V that depends inversely on distance to object, see Figure 14.6.  We will avoid the 0 to 10 cm range where the sensor has the nonmonotonic behavior. We will use two ADC channels (PE4 and PE5) to convert the two analog voltages to digital numbers. Let Left and Right be the ADC digital samples measured from the two sensors.  We can assume distance is linearly related to 1/voltage, we can implement software functions to calculate distance in mm as a function of the ADC sample (0 to 4095). The 241814 constant was found empirically, which means we collected data comparing actual distance to measured ADC values.

                    Dleft = 241814/Left

                 Dright = 241814/Right

Figure 14.8 shows the accuracy of this data acquisition system, where the estimated distance, using the above equation, is plotted versus the true distance.

Figure 14.8. Measurement accuracy of the Sharp GP2Y0A21YK0F distance sensor used to measure distance to wall.

Next we need to extend the robot built in Example 12.2. First we build two motor drivers and connect one to each wheel, as shown in Figure 14.9. There will be two PWM outputs: PA6 controls the right motor attached to the right wheel, and PA5 controls the left motor attached to the left wheel. The motors are classified as actuators because they exert force on the world. Similar to Example 12.2 we will write software to create two PWM outputs so we can independently adjust power to each motor. If the friction is constant, the resistance of the motor, R, will be fixed and the power is

                    Power = (8.42/R)*H/(H+L)

When creating PWM, the period (H+L) is fixed and the duty cycle is varied by changing H. So we see the robot controller changes H, it has a linear effect on delivered power to the motor.

Figure 14.9. Circuit diagram of the robot car. One motor is wire reversed from the other, because to move forward one motor must spin clockwise while the other spins counterclockwise.

The currents can range from 500mA to 1 A, so TIP120 Darlington transistors are used, because they can sink up to 3 A see data sheet. Notice the dark black lines in Figure 14.9; these lines signify the paths of these large currents. Notice also the currents do not pass into or out of the LaunchPad. Figure 14.10 shows the robot car. The two IR sensors are positioned in the front at about 45 degrees.

Figure 14.10. Photo of the robot car.


                     Video 14.7. Autonomous Robot Demonstration

Figure 14.11 illustrates the feedback loop of the control system. The state variables are Dleft and Dright. The two sensors create voltages that depend on these two state variables. The ADC samples these two voltages, and software calculates the estimates Dleft and Dright. Error is the difference between Dleft and Dright. The right motor is powered with a constant duty cycle of 40%, while the duty cycle of the left motor is adjusted in an attempt to drive down the middle of the road.  We will constrain the duty cycle of the left motor to between 30% and 50%, so it doesn’t over compensate and spin in circles. If the robot is closer to the left wall (Dleft < Dright) the error will be negative and more power will be applied to the left motor, turning it right. Conversely, if the robot is closer to the right wall (Dleft > Dright) the error will be positive and less power will be applied to the left motor, turning it left. Once the robot is in the middle of the road, error will be zero, and power will not be changed. This control algorithm can be written as a set of simple equations. The number “200” is the controller gain and is found by trial and error once the robot is placed on the road. If it is slow to react, then we increase gain. If it reacts too quickly, we decrease the gain.

         Error = Dleft - Dright

         LeftH = LeftH – 200*Error;

         if(LeftH < 30*800) LeftH=30*800;  // 30% min

         if(LeftH > 50*800) LeftH=50*800;  // 50% max

         LeftL = 80000 - LeftH;            // constant period


Observation: In the field of control systems, a popular approach is called PID control, which stands for proportional integral derivative. The above simple algorithm actually implements the integral term of a PID controller. Furthermore, the two if statements in the control software implement a feature called anti-reset windup.

These controller equations are executed in the SysTick ISR so the controller runs at a periodic rate.

Figure 14.11. Block diagram of the closed loop used in the robot car.


                     Video 14.6b. The Robot Control System

Details about microcontroller-based control systems can be found in Chapter 10 of  Embedded Systems: Real-Time Operating Systems for ARM® Cortex-M Microcontrollers, 2014, ISBN: 978-1466468863.

Bill of Materials

1) Two DC geared motors, HN-GH12-1640Y,GH35GMB-R, Jameco Part no. 164786
   - 0.23in or 6 mm shaft (get hubs to match)
2) Metal or wood for base,
3) Hardware for mounting
   - 2 motor mounts 1-1/4 in. PVC Conduit Clamps Model # E977GC-CTN Store SKU # 178931
   - some way to attach the LaunchPad (I used an Erector set, but you could use rubber bands)
4) Two wheels and two hubs to match the diameter of the motor shaft
   - Shepherd 1-1/4 in. Caster Rubber Wheel Model # 9487
   - 2 6mm hubs Dave's Hubs - 6mm Hub Set of Two Part# 0-DWH6MM
   - 2 3-Inch Diameter Treaded Lite Flite Wheels 2pk Part# 0-DAV5730
5) Two GP2Y0A21YK IR range sensors
   - Sparkfun, SEN-00242 or
6) Battery
- 8.4V NiMH or 11.1V LiIon. I bought the 8.4V NiMH batteries you see in the video as surplus a long time ago. I teach a real-time OS class where students write an OS then deploy it on a robot. I have a big pile of these 8.4V batteries, so I used a couple for the two robots in this class. NiMH are easier to charge, but I suggest Li-Ion because they store more energy/weight. For my medical instruments, I use a lot of Tenergy 31003 (7.4V) and Tenergy 31012 (11.1V) (internet search for the best price). You will need a Li-Ion charger. I have used both of these Tenergy TLP-4000 and Tenergy TB6B chargers.
7) Electronic components
   - two TIP120 Darlington NPN transistors
   -2 1N914 diodes
   -2 10uF tantalum caps
   - 7805 regular
   -2 10k resistors

Websites to buy robot parts

Robot parts

Pololu Robots and Electronics
Jameco's Robot Store
Robot Marketplace
Tower Hobbies

Surplus parts

BG Micro
All Electronics

Full-service parts

Newark (US) or element14 (worldwide)

Part search engine



Reprinted with approval from Embedded Systems: Introduction to ARM Cortex-M Microcontrollers, 2014, ISBN: 978-1477508992,
from Embedded Systems: Real-Time Interfacing to ARM Cortex-M Microcontrollers, 2014, ISBN: 978-1463590154,

and from Embedded Systems: Real-Time Operating Systems for the ARM Cortex-M Microcontrollers , 2014, ISBN: 978-1466468863,


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Embedded Systems - Shape the World by Jonathan Valvano and Ramesh Yerraballi is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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