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Learn how to design, fabricate, test, calibrate, compensate, monitor, and maintain your MEMS sensors to achieve high performance and quality.
Learn how to manage and scale sensor data storage and processing in cloud-based or hybrid IoT architectures with these best practices and tools.
Learn about the differences, benefits, and drawbacks of active and passive sensors, and how to select the most suitable one for your application.
Learn how to extract and classify features from sensor signals from different modalities, such as audio, video, or bio-signals.
Learn how to select and evaluate the appropriate MEMS and nanosensors for your project or product. Discover the types, performance, design, and feedback of these…
Learn how to compare and contrast different types of temperature sensors based on their categories, principles, advantages, and disadvantages. Find out some…
Learn the six essential skills and competencies that you need to master for RFID and NFC applications. Find out how to design, develop, analyze, secure…
Learn about the features and criteria to look for in sensor network simulation software, such as accuracy, scalability, flexibility, validation, analysis, and…
Learn how to design and implement a sensor network for distributed PID control, a technique that uses multiple sensors and actuators to regulate a complex system.
Learn how to improve your sensor design and fabrication by following these best practices for sensor packaging and integration with other components.
Learn how sensor fusion can support environmental monitoring and disaster response efforts by combining data from different sensors.
Learn how to manage and evaluate the trade-off between data quality and network lifetime in wireless sensor networks with various techniques and methods.
Learn about the benefits and challenges of data aggregation and compression for wireless sensor networks, and discover some of the most suitable techniques for your…
Learn about the best practices for sensor data compression and aggregation, and how they can improve the performance and energy efficiency of sensor networks.
Learn how to use sensor error and uncertainty information to improve decision making and control in your sensor-based applications.
Learn how to deal with sensor error and uncertainty in machine learning using techniques such as UQ, imputation, fusion, validation, and preprocessing.