Sensor Signal Processing

Sensor signal processing is a field that focuses on converting raw sensor data into a form that can be interpreted by systems and humans alike. It includes filtering noise, calibrating signals, and extracting valuable features from data.

This is the third in a series of Special Issues dedicated to Sensor Signal and Information Processing SSIP . The first two series of SSIP were published in 2018 and 2019, respectively 2,3,4. SSIP has become an overarching field of research that is focused on the mathematical foundations and practical applications of signal processing

Sensor Signal Processing. Signal processing is the analysis and manipulation of signals generated by sensors. Effective signal processing ensures data reliability and accuracy, enhancing the functionality of the sensor circuit. 1. Noise Reduction. Noise is an unwanted disturbance that can corrupt the signal generated by a sensor.

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Sensor data analysis. Signal processing is at the core of sensor data analysis, which is used in fields like environmental monitoring, weather forecasting, and industrial automation. It helps identify patterns and trends in sensor data, allowing predictive maintenance, anomaly detection, and optimization of systems and processes.

An increasing number of applications require the joint use of signal processing and machine learning techniques on time series and sensor data. MATLAB can accelerate the development of data analytics and sensor processing systems by providing a full range of modelling and design capabilities within a single environment.

Abstract. This Special Issue compiles a set of innovative developments on the use of sensor signals and information processing. In particular, these contributions report original studies on a wide variety of sensor signals including wireless communication, machinery, ultrasound, imaging, and internet data, and information processing methodologies such as deep learning, machine learning

The processing of the data and the consequent extraction of useful information are also vital and included in the topics of this Special Issue. This Special Issue of Sensors aims to highlight advances in the development, testing, and application of data-, signal-, and image-processing algorithms and techniques to all types of sensors and

Digital Signal Processing DSP is one of those technologies that play the crucial role in the development of the entire world today. DSP helps real world analog signals such as speech, video, or sensor signals to be converted into digital signals and processed, filtered and analyzed through computational methods. Some of the most

In this article, a general introduction to the area of sensor array and multichannel signal processing is provided, including associated activities of the IEEE Signal Processing Society SPS Sensor Array and Multichannel SAM Technical Committee TC. The main technological advances in five SAM subareas made in the past 25 years are then presented in detail, including beamforming, direction

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Signal processing of sensor data Data is often recorded as time signals from a wide variety of sensors. These sensors record physical phenomena such as temperature, pressure or light and convert them into electrical signals that can then be further processed. The ability to precisely record and analyze these signals is crucial for applications