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"IEEE Sensors Alert" is a pilot project of the IEEE Sensors Council. Started as one of its new initiatives, this weekly digest publishes teasers and condensed versions of our journal papers in layperson's language.
Environment mapping is a key component in assisted and autonomous driving. This paper introduces a method to generate 2D occupancy maps using light detection and ranging (LiDAR) and radar data, leveraging their clustered, sparse nature. It presents a linear sensor measurement model and pattern-coupled sparse Bayesian learning approach for occupancy map estimation. Tested with real-world data highlighting the methods, it shows superior performance in detecting obstacles.
Electronic textiles (E-textiles) have recently emerged as a promising technology and will soon transform the wearable industry. Due to its flexibility and ease of embedding in garments, wearable sensing has become a favourite choice for continuous health monitoring of athletes and medical patients. Advancements in textile-based sensors significantly impact the quality of life and will play an important role in the field of Internet of Things (IoT).
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