Special Issue "Intelligent Sensor Signal in Machine Learning"
Deadline for manuscript submissions: 31 August 2019.
Prof. Dr. ByoungChul Ko
Dept. of Computer Engineering, Shindang-Dong, Dalseo-Gu, Daegu, Keimyung Univ. 704-701, Korea
Website | E-Mail
Interests: advanced driver assistant system; human detection and tracking (thermal); analysis of remote sensing images; human action recognition; fire and smoke detection; medical image processing
With the advancement of sensor technology, research has been actively carried out to fuse sensor signals and to extract useful information for various recognition problems based on machine learning. Recently, we have been obtaining signals from various sensors, such as wearable sensors, mobile sensors, cameras, heart rate monitoring devices, EEG head-caps and headbands, ECG sensors, breathing monitors, EMG sensors, and temperature sensors. However, as the sensor signal itself has no meaning, the machine learning algorithm must be combined in order to process the signals and make various decisions. Therefore, the use of machine learning, including deep learning, is appropriate for these challenging tasks.
The purpose of this Special Issue is to take the opportunity to introduce the current developments of intelligent sensor applications and innovative sensor fusion techniques combined with machine learning, including computer vision, pattern recognition, expert systems, deep learning, and so on. In this Special Issue, you are invited to submit contributions of original research, advancement, developments, and experiments pertaining to machine learning combined with sensors. Therefore, this Special Issue welcomes the newly developed methods and ideas combining the data obtained from various sensors in the following fields (but not limited to these fields):
- Sensor fusion techniques based on machine learning
- Sensors and big data analysis with machine learning
- Autonomous vehicle technologies combining sensors and machine learning
- Wireless sensor networks and communication based on machine learning
- Deep network structure/learning algorithm for intelligent sensing
- Autonomous robotics with intelligent sensors and machine learning
- Multi-modal/task learning for decision-making and control
- Decision algorithms for autonomous driving
- Machine learning and artificial intelligence for traffic/quality of experience management in IoT
- Fuzzy fusion of sensors, data, and information
- Machine learning for IoT and sensor research challenges
- Advanced driver assistant systems (ADAS) based on machine learning
- State-of-practice, research overview, experience reports, industrial experiments, and case studies in the intelligent sensors or IoT
Prof. Dr. ByoungChul Ko
Dr. Deokwoo Lee
Manuscript Submission Information
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