Data Acquisition and Signal Processing for Smart Sensors
From simple thermistors to intelligent silicon microdevices with powerful capabilities to communicate information across networks, sensors play an important role in such diverse fields as biomedical and chemical engineering to wireless communications. Introducing a new dependent count method for frequency signal processing, this book presents a practical approach to the design of signal processing sensors. Modern advanced microsensors technologies require new and equally advanced methods of frequency signal processing in order to function at inreasingly high speeds. The authors provide a comprehensive overview of data acquisition and signal processing methods for the new generation of smart and quasi--smart sensors. The practical approach of the text includes coverage of the design of signal processing methods for digital, frequency, period, duty--cycle and time interval sensors. aeo Contains numerous practical examples illustrating the design of unique signal processing sensors and transducers aeo Details traditional, novel, and state of the art methods for frequency signal processing aeo Coverage of the physical characteristics of smart sensors, development methods and applications potential aeo Outlines the concept, principles and nature of the method of dependent count (MDC); a unique method for frequency signal processing, developed by the authors This text is a leading edge resource for measurement engineers, researchers and developers working in microsensors, MEMS and microsystems, as well as advanced undergraduates and graduates in electrical and mechanical engineering.
Why Read This Book
You should read this book if you design sensor front-ends or embedded acquisition systems and want a practical bridge between analog conditioning and digital signal processing. It highlights real-world issues (ADC choice, anti-aliasing, noise, calibration) and introduces frequency-domain methods tailored to smart sensors so you can build higher-performance, networked sensing nodes.
Who Will Benefit
Embedded and hardware engineers, DSP practitioners, and sensor designers who need to integrate analog front-ends with digital processing for smart or quasi-smart sensor systems.
Level: Intermediate — Prerequisites: Basic signals and systems, elementary analog electronics (op-amps, filters), and an introductory understanding of sampling/ADC concepts; familiarity with basic DSP concepts or MATLAB is helpful.
Key Takeaways
- Design complete data-acquisition chains including sensor conditioning, anti-aliasing, and ADC selection suitable for embedded sensors.
- Analyze and mitigate noise sources in sensor systems to improve SNR and measurement fidelity.
- Apply frequency-domain processing techniques (including the book's dependent-count approach) to sensor signal analysis and detection.
- Implement practical calibration and compensation strategies to make sensor outputs reliable in real deployments.
- Integrate digital processing and communication interfaces for smart sensors and understand tradeoffs in embedded implementations.
Topics Covered
- Introduction to Smart Sensors and System Requirements
- Overview of Sensor Types and Signal Characteristics
- Analog Signal Conditioning: Amplification and Filtering
- Sampling Theory and ADC Architectures
- Anti-aliasing Filter Design for Sensor Front-Ends
- Noise, Dynamic Range, and SNR Considerations
- Frequency Signal Processing and the Dependent-Count Method
- Digital Filtering and Spectral Analysis for Sensors
- Data Acquisition System Design and Architectures
- Microcontrollers, DSP Processors and Embedded Implementation
- Calibration, Compensation, and Error Modeling
- Communications and Networking for Smart Sensors
- Case Studies and Application Examples
- Future Trends in Smart Sensor Technology
Languages, Platforms & Tools
How It Compares
Overlaps with sensor-focused references like Fraden's 'Handbook of Modern Sensors' and Pallas-Areny & Webster's 'Sensors and Signal Conditioning', but it places more emphasis on frequency-domain processing and practical DAQ design for embedded smart sensors.
