Measurement

Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material representing achievements in the field, whose ultimate goal is an enhancement of the state-of-the-art of subjects such as: measurement and metrology fundamentals, measurement science, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures for performance analysis of measurement systems, processes and algorithms, mathematical models for measurement-oriented purposes, and distributed measurement systems in a connected world. Notes:

Papers including measurement results that, although important to validate any given scientific study but which offer no new insights in an area different from measurement science or technology, do not fall within the scope of this journal;

The disciplined usage of well-known metrological terms is strongly required. Authors can access information on all relevant terms such as measurement accuracy, uncertainty, the law of propagation of uncertainty and other, similar terms: these are defined in internationally approved guidelines such as the International Vocabulary of Metrology (VIM) and Guide to the Expression of Uncertainty in Measurement (GUM), which are freely available on https://www.bipm.org/en/publications/guides/;

The paper must clearly describe the measurement context in which the research was carried out by undertaking a critical review of the state-of-the-art of the relevant body of knowledge in instrumentation and measurement and by showing how the research presented advances it;

The letter accompanying the submission must describe clearly how the paper satisfies the above requirements.

Papers that focus on image processing or fault diagnosis with little or no elements of measurement science or technology will not be considered within the journal scope. Authors please note: The journal Measurement is receiving an increasing number of papers in the area of machine learning/neural networks and other techniques based on artificial intelligence. These submissions will be desk rejected unless they:

prove that the described research advances the state-of-the-art in measurement science and is not just an application of an available tool to known or novel problems, that is used without an appreciation of measurement-related aspects;

show that the usage of these tools is put into the correct measurement-related context and not just in the context of machine-learning/neural network applications;

contain enough information about the used tools, data, and results to allow, in principle, anyone to replicate the described results;

display the use of specific metrics to strengthen the results of research activities.

It must be recalled that Measurement is interested in publishing new methods, techniques, procedures, algorithms, and alike that show how to better measure in nature and in the world. Thus, the capability to describe metrological-related details of the proposed research represents a major difference between papers published by this journal and by other journals publishing papers on similar topics. This major difference must be evident also in papers covering applications of machine learning and soft computing techniques. Failing to adhere to these guidelines will result in a paper desk-reject decision. Authors whose manuscripts focus on the research, development and application of the science, engineering and technology of sensors and sensor systems, are welcome to submit to the journal's open access companion title, Measurement: Sensors. Authors whose manuscripts focus on food and nutrition measurements may also wish to submit to the journal's second open access companion title, Measurement: Food.