Signal Processing and Machine Learning for Smart Sensing Applications (Volume II)
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 1060
Special Issue Editors
Interests: adaptive signal processing; machine learning; IoT; noise cancellation
Special Issues, Collections and Topics in MDPI journals
Interests: neural networks; pattern recognition; machine learning; image processing; outdoor robotics; artificial intelligence; indoor localization and positioning
Special Issues, Collections and Topics in MDPI journals
Interests: wireless localization and sensing; signal processing and detection; machine learning and information fusion
Special Issues, Collections and Topics in MDPI journals
Interests: GNSS; multi-Source positioning; radio navigation
Special Issues, Collections and Topics in MDPI journals
Interests: multisensory fusion for indoor/outdoor pedestrian positioning; GNSS positioning in urban environments; integrity monitoring
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This publication is a continuation of our previous Special Issue on the same topic, entitled “Signal Processing and Machine Learning for Smart Sensing Applications (Volume II)”.
This Special Issue focuses on advanced signal processing and machine learning technologies for smart sensing applications. Successful examples include radio navigation, indoor/outdoor positioning, mm wave sensing, speech denoising, noise cancellation, in-home monitoring technologies for ageing in place, biomedical signal processing with wearable devices, etc. One of the objectives of this Special Issue is to present smart sensing applications that leverage state-of-the-art signal processing and machine learning technologies. The other main purpose is to promote interdisciplinary collaborations between researchers in the fields of signal processing and machine learning technologies for smart sensing applications.
The emerging trends for smart sensing include: (1) the integration of sensors with low-power embedded signal processing into one system, (2) the integration of multiple sensors in the same system to extract more useful data, and (3) the use of compressive sensing techniques to extract useful information from the original sensor output. To achieve these goals, sophisticated signal processing and machine learning technologies are required.
Prof. Dr. Ying-Ren Chien
Dr. Joaquín Torres-Sospedra
Prof. Dr. Mu Zhou
Dr. Ao Peng
Dr. Ni Zhu
Guest Editors
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