ADC measurements applications

1. Introduction: Exploring ADC Measurements for Application-Specific Solutions with STM32 MCUs

Analog-to-Digital Converters (ADCs) are essential components in modern electronic systems, enabling the conversion of physical quantities—such as voltage, current, or temperature—into digital signals that microcontrollers or microprocessors can process. This page evaluates various categories of embedded applications in monitoring and control systems, highlighting the key parameters and factors important for each use case. Understanding these nuances is vital for selecting and applying ADCs correctly, ensuring reliable data acquisition in embedded systems and electronic projects.

This page will help you discover several ADC application categories, knowing which types of measurements can be used in each application as well as which post-processing procedures are most appropriate. Guidelines on the best ADC configuration to perform such types will be detailed and exemplified in the ADC measurements recommendation page.

2. ADC typical applications with analog signal usage

For a general analysis of applications, we divide the categories into 6 main ones: audio processing, automatic control, data acquisition, imaging, radar and sonar and telecommunication. Thus, in the following image, you can see some applications and examples of possible measurements and applications for each category.

For each category and application, there are important ADC measurements to evaluate signal accuracy and for data processing for use in the desired application. Therefore, the following points include examples of essential parameters for the project designer's analysis for each application category.

2.1. Audio Applications

Audio processing

The performance analysis of an ADC (Analog-to-Digital Converter) in audio signals directly depends on its application, as each use case involves specific analysis parameters and post-processing methodologies that enhance the effectiveness of results and implementation. To illustrate, we can compare two simple and widely used applications today: sound level detection and audio recording level monitoring.

The objective of this application is to detect when the audio signal surpasses a certain threshold level or to identify a specific frequency during audio acquisition. Understanding the following parameters is essential:

• AC signal amplitude: Critical for detecting audio signal threshold levels, allowing identification of when sound is present above a defined level.
• SNR (Signal-to-Noise Ratio): Important to ensure that the detected signal corresponds to relevant sound rather than system noise, especially in low-signal environments.
• Sensitivity: Determines the lowest signal level that can be reliably detected, directly affecting the system’s ability to capture weak or distant sounds.

Post-processing may include bandwidth filtering to detect specific sound levels, spectral analysis (spectrogram), and unit conversion to dBSPL (decibels sound pressure level). A practical example is a security camera equipped with a microphone.

• Filtering: Essential for accurate measurement of audio levels, ensuring input signals are neither saturated nor underestimated.
• Spectrogram: Provides a visual representation of the frequency spectrum over time.
• Unit conversion: Facilitates evaluation of subtle audio variations.

For audio recording level monitoring applications, the most important measures to analyze for the ADC are:

• AC signal amplitude: Essential for precise measurement of audio recording levels, avoiding signal saturation or underestimation.
• SNR (Signal-to-Noise Ratio): Helps verify that recording levels are analyzed without significant noise interference, particularly in low-signal environments.
• Sensitivity: Allows detection and evaluation of subtle audio variations.
• THD (Total Harmonic Distortion): Ensures the recorded signal maintains sound integrity without introducing unwanted distortion.
• SINAD (Signal-to-Noise and Distortion): Provides an overall assessment of the recorded signal’s quality.

Post-processing techniques may include bandwidth filtering, reconstruction filtering to smooth output signals, and unit conversion to dB or dBSPL. A common example of this application is an audio mixer.

• Filtering: Crucial for accurate audio level measurement, preventing saturation or underestimation.
• Reconstruction filter: Used to minimize noise interference and produce smooth output signals.
• Unit conversion: Enables detailed evaluation of subtle audio variations.

2.1.1. Summary table

Considering these applications, the table below presents the important types of measurements to be made when using the ADC for each type of application, along with the recommended post-processing methods.

Audio Applications	Measurements types					Post - processing methods
	Voltage AC amplitude	SNR	Sensitivity	THD	SINAD	Filtering	Spectrogram	Unit conversion	Reconstruction Filter
Sound level detection	✓	✓	✓			✓	✓	✓
Audio recording level monitoring	✓	✓	✓	✓	✓	✓		✓	✓

Table 1 - Common measurement types and post-processing methodologies for each example of audio application.

Both applications rely on accurate ADC measurements of AC signal amplitude, SNR, and sensitivity to capture relevant audio signals. Sound level detection focuses on identifying threshold crossings and specific frequencies, often in noisy or low-level environments. Audio recording monitoring requires more detailed analysis, including distortion metrics like THD and SINAD, to ensure sound integrity and quality. Post-processing in detection emphasizes filtering and spectral analysis, while recording monitoring adds reconstruction filtering and precise unit conversion for high-fidelity audio capture.

2.2. Automatic control applications

Industrial automated robot arms

Automatic control applications involve equipment operating according to specific parameters or states without external intervention. These systems are common in industrial settings, such as machine operation, and everyday scenarios like room temperature control.

Accurate and real-time measurement of key parameters—such as voltage, current, and position—is essential for implementing effective control strategies. ADCs play a critical role by converting these analog signals into digital data, enabling precise monitoring that ensures optimal performance, efficiency, and reliability in systems like motor controls and robotics.

In this section, we will explore examples of motor control and battery voltage monitoring.

The objective of battery voltage monitoring is to continuously assess the voltage levels of a battery to ensure optimal performance and longevity. Accurate voltage measurement allows the system to determine the state of charge (SOC) and state of health (SOH), enabling timely detection of overcharging or deep discharge conditions that could cause battery degradation or failure. This monitoring relies on precise ADC measurements to detect subtle voltage changes, ensuring the battery operates within safe and efficient parameters.

Key parameters include:

• DC Voltage: Accurate DC voltage measurements are essential for assessing battery condition and determining when charging or discharging is required.
• DC Current: Monitored via a shunt resistor, it ensures the battery operates within safe limits and aids in energy management.
• Ripple: Elevated noise or ripple levels may indicate issues with the battery or power supply, potentially causing inaccurate readings or damage.
• SNR: High SNR is crucial for reliable voltage and current measurements, especially in electrically noisy environments.
• Total Harmonic Distortion (THD): THD monitoring helps ensure power quality and prevents battery stress caused by distorted waveforms.

Post-processing techniques focus on smoothing voltage readings to highlight trends and applying thresholding to detect critical voltage levels indicative of battery health. Typical methods include:

• Data Smoothing: Techniques such as spline fitting or polynomial regression to emphasize trends.
  • Averaging: Smooths short-term fluctuations to reveal clearer long-term trends.
• Filtering: Removes short-term noise to clarify the signal.
• Outlier Detection: Identifies and excludes spurious readings caused by glitches or transient noise.
• Thresholding: Sets voltage thresholds to detect specific battery states like overcharge or deep discharge.

In motor control applications, ADCs measure key parameters such as motor current, voltage, and feedback signals from position sensors or encoders. These measurements are vital for implementing precise control algorithms that adjust motor inputs to achieve desired performance. Accurate, real-time data enables the control system to respond effectively to load changes, maintaining optimal motor operation for efficiency and safety across industrial and consumer uses. Important parameters include:

1. DC or static parameters:
   • DC Voltage: Essential for determining motor input voltage or DC bus voltage in variable frequency drives (VFDs).
   • DC Current: Measured via a shunt resistor, it assesses power consumption and motor efficiency.
2. AC or dynamic parameters:
   • AC Voltage and Current: Critical for monitoring AC motor performance, ensuring supply voltage and current remain within specified limits.
   • Total Harmonic Distortion (THD): Assesses AC signal quality; high THD can reduce efficiency and damage the motor.
   • Ripple: In DC motors, ripple voltage may indicate power supply or rectification issues affecting performance.

Post-processing methods commonly used include:

• Averaging: Reduces random noise by averaging multiple samples, improving SNR.
• Filtering (low - pass or band-pass filter):Focuses on relevant frequency ranges.
• Normalization: Scales signals to a standard range for easier comparison across measurements or systems.
• Peak/ Envelope Detection:Detects transient events or anomalies in motor operation.

2.2.1. Summary table

Automatic control Applications	Measurements types					Post - processing methods
	AC Voltage/ Current	DC Voltage	DC Current	Ripple Amplitude	THD	Averaging Filtering	Normalization	Outlier Detection	Peak/Envelope Detection	Thresholding
Battery voltage monitoring		✓	✓	✓		✓		✓		✓
Motor Control	✓	✓	✓	✓	✓	✓	✓		✓

Table 2 - Common measurement types and post-processing methodologies for each example of automatic control application.

Both applications rely on accurate ADC measurements of voltage and current to ensure system safety and efficiency. Battery monitoring emphasizes DC parameters and power quality metrics like THD and ripple to maintain battery health. Motor control requires both DC and dynamic AC measurements, focusing on real-time monitoring of voltage, current, and signal quality to optimize motor performance. Post-processing in battery monitoring targets trend smoothing and threshold detection, while motor control prioritizes noise reduction, normalization, and transient event detection.

2.3. Data acquisition applications

Worker Using An Ultrasonic Pipeline Flow Meter

Data acquisition applications are designed to collect and analyze information from various sensors and instruments, enabling detailed monitoring of parameters such as temperature, pressure, and sound. These applications are vital in industries like research, diagnostics, and quality control, where accurate and timely data supports informed decision-making. While data acquisition focuses on gathering and analyzing sensor data, automatic control applications use this data to regulate system operations in real time.

The objective of flowrate metering using the ultrasound Doppler technique is to precisely measure the velocity and flow rate of fluids within pipes or open channels. This method leverages the Doppler effect, where ultrasound waves are transmitted into the fluid and their frequency shift upon reflection from particles or bubbles is analyzed. The frequency shift is directly proportional to the fluid’s velocity, enabling accurate flow rate calculation.

The ADC plays a crucial role by converting the analog ultrasound signals into digital data for precise analysis. Key parameters include:

1. AC or dynamic parameters
   • Frequency: Accurate measurement of the Doppler frequency shift is essential to determine fluid velocity.
   • Amplitude of the ultrasound signal: Indicates the strength of the reflected signal and helps assess measurement quality.

Post-processing techniques include:

• Averaging: Reduces random noise by averaging multiple samples, improving signal-to-noise ratio (SNR).
• Envelope Detection: Extracts amplitude modulation to aid flow rate identification.
• Filtering:
  • Band-pass Filter: Isolates Doppler-shifted frequencies, removing unwanted noise and interference.
  • Low-pass Filter: Reduces high-frequency noise affecting signal accuracy.
• Fast Fourier Transformation (FFT) Analysis: Converts time-domain signals to frequency domain for Doppler shift identification.
• Phase Correction: Adjusts signal phase to correct measurement errors, enhancing Doppler shift accuracy.

Seismic monitoring aims to detect and analyze ground vibrations caused by natural events like earthquakes or human activities such as mining. Sensors capture seismic waves, which ADCs convert into high-resolution digital data for detailed waveform and frequency analysis.

Important parameters include:

1. AC or dynamics parameters:
   • Amplitude: Critical for assessing the magnitude of seismic events.
   • Frequency: Frequency domain analysis helps identify characteristic frequencies of different seismic phenomena.

Post-processing methods in seismic monitoring include filtering to eliminate ambient noise, spectral analysis to identify the characteristic frequencies of seismic events, and time-domain analysis to assess the magnitude and impact of detected movements. These techniques ensure accurate detection and interpretation of seismic activity, facilitating timely alerts and risk assessments. Post-processing may include:

• Data Compression: Techniques like Principal Component Analysis (PCA) reduce data size while preserving key features for efficient storage and transmission.
• Event Detection: Algorithms automatically detect and classify seismic events based on signal characteristics.
• Fast Fourier Transformation (FFT) Analysis: Identifies frequency content to distinguish seismic event types.
• Filtering:
  • High-pass Filter: Removes low-frequency noise such as drift or baseline shifts.
  • Notch Filter: Eliminates interference from known sources like power line noise.
• Signal Denoising:
  • Wavelet Transform: Separates noise from signal while preserving important seismic features.
  • Adaptive Filtering: Dynamically reduces noise based on changing environmental conditions.

2.3.1. Summary table

Data Acquisition Applications	Measurements types		Post - processing methods
	AC amplitude	Frequency	Filtering	Data Compression	Envelope Detection	Event Detection	FFT	Phase Correction	Signal Denoising
Flowrate metering	✓	✓	✓		✓		✓	✓
Seismic monitoring	✓	✓	✓	✓		✓	✓		✓

Table 3 - Common measurement types and post-processing methodologies for each example of data acquisition application.

In summary, both applications use ADCs for frequency and amplitude analysis. Flowrate metering focuses on precise frequency shift measurement and signal averaging to enhance detection. Seismic monitoring prioritizes noise reduction, data compression, and event detection to manage complex signals and large data sets.

2.4. Imaging applications

Imaging and video applications capture and process visual information for purposes such as surveillance, medical diagnostics, and lighting control systems. These applications depend on converting analog signals from cameras into digital formats, enabling enhanced processing, analysis, and storage. By ensuring high-resolution and accurate data capture, they support detailed visualization and real-time video processing, driving advancements in image quality and system performance.

The objective of a light control system is to manage and optimize lighting conditions to enhance energy efficiency, comfort, and functionality across various environments, including residential, commercial, and industrial settings. This system regulates light intensity, color, and distribution to meet specific needs.

ADCs play a vital role by converting analog signals from light sensors into digital data, enabling precise real-time adjustments of lighting parameters based on ambient conditions.

1. DC or static parameters
   • DC Voltage: Monitors voltage levels supplied to lighting circuits to ensure operation within safe limits, maintaining consistent lighting.
   • DC Current: Measures current flow to evaluate power consumption and system efficiency.

Post-processing techniques commonly applied in light control systems include:

• Filtering:
  • Low-pass Filter: Removes high-frequency noise to improve measurement precision.
  • Notch Filter: Eliminates interference from specific frequency bands, such as power line noise.
• Signal Averaging: Improves measurement stability and reduces noise, which is crucial for accurate light sensing.
• Normalization: standardizes data for better processing and control algorithm performance.

2.4.1. Summary table

Imaging Applications	Measurements types		Post - processing methods
	DC Voltage	DC Current	Filtering	Normalization
Light control system	✓	✓	✓	✓

Table 4 - Common measurement types and post-processing methodologies for each example of imaging application.

2.5. Radar and sonar applications

Military radar air surveillance on navy ship

Radar and sonar applications detect and analyze objects using sound or electromagnetic waves, crucial for navigation, defense, and environmental monitoring. They convert received signals into digital data to accurately assess distance, speed, and direction. This capability allows for precise object detection and tracking, enhancing situational awareness and decision-making in complex environments.

The objective of submarine detection is to identify and track underwater vessels and objects using sonar technology. This process involves emitting sound waves into the water and analyzing the echoes reflected from submarines or other underwater features. ADCs are essential in this process, converting analog sonar signals into digital data for precise analysis of frequency, amplitude, and phase shifts.

1. DC or static parameters
   • DC Voltage:Ensures a stable power supply to sonar equipment, critical for consistent operation.
2. AC or dynamics parameters:
   • AC amplitude: Assesses the strength of sonar echoes, aiding in determining the size and distance of detected objects.
   • Frequency: In systems using modulated light sources (e.g., infrared LEDs), frequency measurement ensures modulation remains within the designed range for optimal sensor performance.
   • Dynamic range: The range between the smallest and largest signals the sonar can detect accurately. A wide dynamic range allows the system to capture both weak distant echoes and strong nearby signals without distortion, improving target detection and classification.

Post-processing techniques include:

• Filtering:
  • Band-pass Filter: Isolates the frequency range of interest to focus on sonar signals while removing noise and interference.
  • Adaptive Filtering: Dynamically adjusts filters to minimize ambient marine noise and enhance signal clarity.
• Data smoothing - Averaging: Improves signal-to-noise ratio (SNR) by averaging multiple sonar samples, reducing random noise.
• Spectral Analysis: Utilizes FFT or wavelet transforms to analyze frequency content, helping identify characteristic frequencies of underwater objects.
• Time-Gating: Focuses on specific time windows to isolate echoes from targets and reduce interference.
• Thresholding: Sets detection thresholds to identify significant echoes indicative of submarine presence, filtering out background noise.

Weather monitoring involves collecting and analyzing atmospheric data to predict weather conditions and track environmental changes. Various sensors measure parameters such as temperature, humidity, wind speed, and pressure. ADCs convert these analog signals into digital data, enabling detailed analysis and forecasting.

1. DC or static parameters
   • DC Voltage: Ensures stable power supply to sensors and data acquisition systems.
   • DC Current: Monitors power consumption of weather monitoring equipment.
2. AC or dynamics parameters:
   • Frequency: Used in Doppler radar systems to assess wind speed and direction.

Post-processing techniques include:

• Filtering:
  • Low-pass Filter: Removes high-frequency noise from sensor data, enhancing precision and reliability.
  • Notch Filter: Eliminates interference from specific frequency bands, such as power line noise.
• Data Smoothing: Applies techniques like spline fitting or polynomial regression to highlight trends and reduce fluctuations.
• Anomaly Detection: Implements algorithms to identify unusual patterns, indicating potential extreme weather events or sensor malfunctions.

2.5.1. Summary table

Radar and Sonar Applications	Measurements types						Post - processing methods
	DC Voltage	DC Current	AC Amplitude	Frequency	Phase Measurement	Dynamic Range	Filtering	Anomalies detection	FFT	Thresholding	Time-Gating
Submarine detection	✓		✓	✓	✓	✓	✓		✓	✓	✓
Weather monitoring	✓	✓		✓			✓	✓

Table 5 - Common measurement types and post-processing methodologies for each example of radar and sonar application.

Both applications rely on stable DC power and frequency measurements via ADCs. Submarine detection demands a wide dynamic range and signal averaging to handle varied sonar echoes and noisy underwater environments. Weather monitoring focuses on filtering noise and smoothing data to identify trends and anomalies. Thus, submarine detection emphasizes dynamic signal clarity, while weather monitoring prioritizes stable, trend-based analysis.

2.6. Telecommunication applications

Telecommunication applications enable the transmission and reception of information over long distances using technologies such as radio, fiber optics, and satellites. By converting analog signals into digital data, these systems ensure efficient, reliable, and high-speed communication. This digital conversion supports robust connectivity and seamless network operations, facilitating global communication across diverse platforms and environments.

RF signal processing involves manipulating and analyzing radio frequency signals to enhance the performance and reliability of wireless communication systems. The goal is to efficiently transmit and receive data over wireless channels while maintaining signal integrity, minimizing interference, and optimizing bandwidth usage. This includes filtering, modulation, demodulation, and amplification of signals to prepare them for transmission or reception. ADCs convert incoming analog RF signals into digital data, enabling advanced digital signal processing techniques.

Key parameters:

1. DC Parameters
   • DC Voltage: Ensures a stable power supply to RF components, critical for consistent operation.
2. AC Parameters
   • Frequency: Essential for analyzing RF signals, as different frequencies carry distinct information.
   • Amplitude: Assesses the strength of RF signals, impacting signal quality and integrity.

Post-processing techniques typically include:

• Filtering:
  • Band-pass Filter: Isolates specific frequency bands while removing unwanted noise.
  • Adaptive Filtering: Dynamically adjusts filters to minimize interference.
• Spectral Analysis: Uses FFT to analyze frequency content and identify key components.
• Averaging: Enhances signal-to-noise ratio (SNR) by averaging multiple samples.

Satellite communication enables data transmission between ground stations and satellites orbiting Earth. It supports reliable long-distance communication for broadcasting, internet access, and global positioning. This process involves modulation, transmission, and reception of signals between satellites and terrestrial systems. ADCs convert analog signals received from satellites into digital format for accurate data extraction and processing.

Key parameters:

1. DC Parameters
   • DC Voltage: Ensures stable power supply to satellite communication equipment.
2. AC Parameters
   • Amplitude: Assesses the strength of satellite signals, affecting communication quality.
   • Frequency: Critical for analyzing transmitted and received signals, ensuring proper channel allocation.

Post-processing techniques include:

• Filtering:
  • Band-pass Filter: Isolates frequency bands specific to satellite signals.
  • Adaptive Filtering: Minimizes interference from other signals.
• Spectral Analysis: Uses FFT to analyze frequency content.

2.6.1. Summary table

Telecommunication Applications	Measurements types			Post - processing methods
	DC Voltage	AC Amplitude	Frequency	Filtering	FFT
RF Signal Processing	✓	✓	✓	✓	✓
Satellite Communication	✓	✓	✓	✓	✓

Table 6 - Common measurement types and post-processing methodologies for each example of telecommunication application.

Both RF signal processing and satellite communication rely on key parameters such as DC voltage stability, frequency, and amplitude measurements to ensure effective signal transmission and reception. However, their operational environments and signal characteristics dictate differences in post-processing priorities.

In RF signal processing, signal averaging is crucial due to the highly variable and noisy nature of wireless channels, helping to improve the signal-to-noise ratio and maintain signal integrity. Adaptive filtering and spectral analysis further enhance the system’s ability to manage interference and extract meaningful data.

Conversely, satellite communication signals are generally more stable over long distances, reducing the need for signal averaging. Instead, emphasis is placed on precise amplitude and frequency measurements combined with filtering and spectral analysis to maintain communication quality and properly allocate channels.

In summary, while both applications share fundamental measurement and processing techniques, RF signal processing requires more aggressive noise reduction strategies like signal averaging, whereas satellite communication focuses on maintaining signal fidelity through stable power supply and targeted filtering

3. Conclusion

This page has provided an overview of various ADC application categories within embedded monitoring and control systems, highlighting the key measurements that influence application analysis. By examining different measurement types along with their associated post-processing and verification methods, users can achieve more accurate data acquisition by leveraging specific ADC information tailored to each application.

The included practical examples and summary table serve as valuable references to guide the usage of ADCs in real-world scenarios. By following these guidelines, users can enhance the reliability and precision of data acquisition in their embedded systems and electronic projects.

For detailed guidelines and practical recommendations on configuring the ADC for various types of measurements, please refer to the ADC measurements recommendation page.