Innovative Data Analysis Techniques for Metrology Applications

Dear Colleagues,

At present, a variety of applications—ranging from robotics to biomechanics, industrial processes to agriculture, medicine to management—adopt smart and innovative techniques to interpret and analyze data. In the era of big data, it is of fundamental importance to robustly acquire information that will later be processed and adopted by the application of interest. As such, metrology plays an important role in both data acquisition and analysis.

Sensing technologies include contactless measurements, wearable sensors, mechanical and thermal instrumentation, and electrical measurements. The same data may be acquired in different ways, and even the same sensing technology may result in different outcomes when processed using different approaches. Furthermore, smart devices and embedded systems are rapidly becoming a necessity, especially for data collection and analysis in unstructured environments. Finally, data validation adhering to statistical and metrological standards is typically key to successful application development.

The aim of this Special Issue entitled “Innovative Data Analysis Techniques for Metrology Applications” is to bring together the research communities interested in metrological applications and data analysis from various departments and universities, without posing a restriction on the application field and especially focusing on real physical quantities instead of simulated models and synthetic datasets.

Topics of interest for this Special Issue include, but are not limited to, the following:

• Applications for agriculture;
• Environmental measurements and data analysis;
• Industrial process measurements;
• Healthcare, rehabilitation, and sport-centered applications;
• Robotics applications;
• Applications in unstructured environments;
• Computer vision, augmented reality, and virtual reality applications;
• Contactless measurements;
• Embedded systems;
• Innovative sensing devices;
• Wearable sensors;
• Deep learning, machine learning, and innovative algorithms and models for data analysis.

The technical program committee member is as follow:

Dr. Alessandro Luchetti
MIRo (Measurement, Instrumentation and Robotics) Laboratory, Department of Industrial Engineering (DII), University of Trento, 38123 Trento, Italy

Dr. Cristina Nuzzi
Prof. Dr. Keiichi Yasumoto
Dr. Yuki Matsuda
Guest Editors

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• measurements
• sensors
• data analysis
• machine learning
• embedded systems
• computer vision
• robotics
• industry
• healthcare
• agriculture
• environmental measurements