Revolutionizing society with cutting-edge data analysis!
"AutoStatSpectra" is a spectral decomposition tool that supports a wide range of analytical methods such as XRD, SAXS, XPS, and NIR. By utilizing automatic peak separation and confidence evaluation based on Bayesian inference, it achieves high-precision and reproducible analysis that was difficult to accomplish with traditional manual methods. Complex spectral data can be processed with just a few clicks, providing an environment where researchers and technicians can focus on their original analysis and discussions. 【Features】 ■ Automatic peak estimation and parameter estimation ■ Support for high-noise data ■ Capable of analyzing large datasets ■ Automatic peak separation with a single click ■ Objectivity and reproducibility in spectral analysis ■ Elimination of subjectivity and acceleration of processes *For more details, please download the PDF or feel free to contact us.
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For more details, please download the PDF or feel free to contact us.
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<Main Use Cases> ■Material Development: Utilized for detailed analysis of surface conditions and compositional changes ■Quality Control: Obtains reproducible results from complex spectral data to verify product stability ■Academic Research: Enables researchers and students to focus on data interpretation through efficient analysis via automation *For more details, please download the PDF or feel free to contact us.
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Our company combines expertise in AI and statistics with knowledge in physics and measurement science to support the automation and labor-saving in research and manufacturing environments. We are a technology startup originating from the University of Tokyo, providing analytical solutions tailored to on-site challenges, such as anomaly detection and algorithm development. Additionally, we build custom analytical infrastructures that consider operations, from problem definition to verification and implementation. We design evaluation metrics and algorithms suited to specific purposes, offering reusable analytical frameworks.
