Data analysis — Transforming data into actionable insights
Raw data holds immense potential, but it takes analysis to unlock its true power. It's not just about collecting data, but about understanding its meaning and applying it effectively.
Just as we interpret weather data like temperature, precipitation or air pressure to make informed decisions about our clothing, data analysis unlocks the hidden potential within raw measurements. Whether analyzing the time-domain behavior of signals in near-field sensing or the frequency-dependent characteristics of liquids, data analysis provides the knowledge and insights needed to make informed decisions and drive innovation.
Understanding the nuances of your application is key, which is why our ultra-wideband analysis leverages both time and frequency domains. This dual approach allows us to tailor the analysis to best reveal the information you need.
Near-field sensing
In near-field sensing, understanding the timing of signals is key. Analysis focuses on how long a signal takes to travel through a medium. This signal is emitted and propagates at a known speed. When it encounters a change in material (for example, from air to an object, or soil to metal), a portion of the signal bounces back. By comparing the emitted and reflected signals, the travel time is calculated, which tells the distance to the object or helps identify the material. Furthermore, analyzing the precise timing of these reflections allows for the detection of movement or measurement of layer thickness.
Liquid analysis
There are two main types of liquid analysis:
- Classification: This is used to categorize liquids into distinct groups. For example, determining if a sample is oil or water, or if the oil is fresh or old. This approach is ideal for applications where you need to make clear distinctions between different types of liquids.
- Regression: This is used to predict continuous values, such as the concentration of specific ingredients in a liquid. This approach is suitable for applications where you need to quantify the composition of a liquid with greater precision.
To ensure the accuracy and reliability of the analysis, regardless of the method used, cross-validation is routinely performed. This technique helps estimate the prediction error and ensures that the model is not overfitting the data, providing a robust assessment of the analysis quality.