**Preface to "Machine Learning in Sensors and Imaging"**

With the recent spread of smartphones, smartwatches, and smartbands, people are carrying various sensors without realizing: a camera to capture an image, an infrared sensor to measure approach or heart rate, a microphone to sense a voice signal, an optical sensor or ultrasound sensor to sense a fingerprint, an accelerometer and a gravity sensor to measure movement or location, you name it. In addition to this, many CCTVs in our daily life and thermal sensors, which were widely distributed due to COVID-19, cannot be left out. A lot of data are created through this abundance of sensors, and various studies are being conducted to efficiently utilize such big data.

At the same time, machine learning greatly contributes to processing/analyzing this overflowing data and creating new applications. Machine learning has made remarkable progress in the name of deep learning thanks to the development of hardware and the accumulation of data from the late 2000s to the present. This Special Issue, Machine Learning in Sensors and Imaging (https://www. mdpi.com/journal/sensors/special issues/ML-SI), of *Sensors* contains a variety of studies that apply machine learning to efficiently utilize the data obtained through sensors in various fields. We started accepting papers in August 2020 and collected a total of 15 research results by January 2022, and covers various fields that utilize the data obtained from sensors and machine learning technologies, including human activity recognition, fuzzy classification, failure detection, sensor-less estimation, automatic camera calibration, telescope control, object detection, wildfire assessment, shelf auditing, forest monitoring, road management, denoising, and touchscreen.

I was honored to participate in this Special Issue as a guest editor, and I look forward to contributing to the revitalization of machine learning research related to sensors, and opening up a new future.

> **Hyoungsik Nam** *Editor*
