SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.
SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal’s impact.
The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years.
© Thomson Reuters Journal Citation Reports 2015
To calculate the five year Impact Factor, citations are counted in 2014 to the previous five years and divided by the source items published in the previous five years.
© Journal Citation Reports 2015, Published by Thomson Reuters
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Mechanical Systems and Signal Processing (MSSP) is an interdisciplinary journal in Mechanical, Aerospace and Civil Engineering with the purpose of reporting scientific advancements of the highest quality arising from new techniques in sensing, instrumentation, signal processing, modelling and control of dynamic systems. MSSP papers are expected to make a demonstrable original contribution to engineering knowledge, which should be significant in terms of advancement over established methods. Especially sought are papers that include both theoretical and experimental aspects, or that include theoretical material of high relevance to practical applications. MSSP is a leader in its field and research areas covered include:
1. Actuation, Sensing and Control
• Vibration & noise control
• Travelling waves
• Smart-material systems
• Integrated systems
2. Measurement & Signal Processing
• Signal processing for the understanding of mechanical systems
• Full-field vibration/acoustic measurements
• Big data problems
• Nonlinear vibration problems
• Nonlinear normal modes
• Energy harvesting
4. Rotating Machines, Machinery Diagnostics & SHM
• Diagnostics and prognostics
• Rotor dynamics
• Cracks in rotors
• Bearings and gears
5. Uncertainty Quantification
• Probabilistic, interval & fuzzy analysis
• Reliability and robustness
• Bayesian methods
6. Vibrations, Modal Analysis & Structures
• Structural modelling & identification
• Inverse problems
• Operational modal analysis
• Ambient vibration testing
Authors of papers with Machine-Learning or Signal Processing content should see the MSSP guidelines on these subjects: http://media.journals.elsevier.com/content/files/machine-learning-04180327.pdf