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Innovative statistical solutions for today's challenging datasets.

Arcus Analytica provides advanced objective analysis for high variability data sets encapsulated in our software to support industrial, research and academic applications.

From automotive to aerospace, and materials to modelling - a wealth of information, understanding, and potential innovation is encapsulated within continuous data sets. However, contemporary analysis of these data sets has limited innovation through a lack of objectivity and statistical rigor. These challenges are addressed by the Arcus software.

At Arcus Analytica, we are developing the next generation of data analysis tools to unlock the potential of current and future datasets. Using a comprehensive and robust arc-length analysis framework, our tools provide in-depth analysis of datasets, simplify your workflow, and generate insights to drive your project forward.

Our Values

Core Values

At the core of Arcus Analytica is our dedicated team of developers and engineers enhancing and applying the arc-length-based analysis framework, which powers many of the innovative solutions we offer. Consisting of a novel combination of arc-length re-parameterization and signal registration, our analysis framework allows users to tackle a huge range of responses with a single set of tools, such as datasets without a common sampling variable, responses that start or start at different positions, are highly variable or oscillatory, and are non-monotonic in one or more axes.

Integrity
Honesty
Innovation
Leadership

Our Team

Meet our expert team!


Devon Hartlen

Devon Hartlen

Chief Executive Officer

Devon developed the arc-length analysis framework while completing his PhD at the University of Waterloo. His background includes experience in signal processing, material testing, and numerical modelling.


Duane Cronin

Duane Cronin

President

Duane Cronin is a Professor of Mechanical and Mechatronics Engineering at the University of Waterloo, with a unique and globally recognized program in computational injury biomechanics, supported by advanced material characterization and experimental testing.



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