Mar 21, 2018 · We introduce common signal processing methods in MATLAB (including digital filtering and frequency-domain analysis) that help extract descripting features from raw waveforms, and we show how parallel computing can accelerate the processing of large datasets.

As a key tool for sensor data analysis, machine learning is becoming a core part of novel sensor design. Dividing a complete machine learning process into three steps: data pre-treatment, feature extraction and dimension reduction, and system modeling, this paper provides a review of the methods that are widely used for each step.

Aug 23, 2024 · While the potential of ML and AI for signal processing, data interpretation, and hardware control can have important advantages in terms of computational speed, their ability to achieve complex and abstract tasks, or handling highly multidimensional data, ML and AI should be appropriately used and it is not necessarily useful for all application...

In order to obtain relevant and insightful metrics from the sensors signals’ data, further enhancement of the acquired sensor signals, such as the noise reduction in the one-dimensional electroencephalographic (EEG) signals or color correction in the endoscopic images, and their analysis by computer-based medical systems, is needed.

With the increasing availability of on-board and remote computing power, the trend is to go beyond the simple quantification of events and (re)create context from sensor data using statistical signal processing, or as commonly known, machine learning. Within the scope of this tutorial, we highlight the applications of machine learning in ...

Machine and Deep Learning algorithms in combination with mathematical transformations offer effective new ways of approaching the difficult problems of processing sensor signals. The paper’s goal is to propose and validate the data-driven prediction model to analyze sensor data based on machine and deep learning algorithms.

Jun 28, 2021 · We envision a new generation of computational sensing systems that reduce the data burden while also improving sensing capabilities, enabling low-cost and compact sensor implementations...

Jan 1, 2010 · Developing hardware, algorithms and protocols, as well as collecting data in sensor networks are all important challenges in building good systems. We describe a vertical system integration of...

Jan 28, 2023 · Experimental results showed that the proposed algorithm effectively improves signal-to-noise ratio (SNR) and speech quality in different scenarios, and the assistance of BC signals can effectively improve the noise reduction performance of beamforming.

End-to-End Overview of Signal Processing Workflow: The article thoroughly discusses the key tasks in a typical signal processing pipeline, grouping them under three main categories: preprocessing, processing, and application.