Building the Algorit​hms that shape our world

Revealing the Hidden Reality of Information through
Fractional Calculus and AI

View Our 2D PitchDeck 

A Primer on Digital Signal Processing

About Ou​r Company

Information is all around us, but is often invisible, either imperceptible or the signal is lost in the noise. As a research, development, and innovation laboratory, the sNoise Research Laboratory (sNRL) is leading the way to develop plug and play Fractional Calculus analytical tools which include advanced digital signal processing libraries, signal-shaping smart filters, advanced machine/deep-learning toolboxes, and algorithmic mathematical solutions for specific fields-of-use in order to better extract the signal from the noise to reveal the hidden reality of information shaping the technologies of our modern world.

The Science of Noise

The science of noise, or sNoise® (a registered trademark of the sNoise Research Laboratory) is the science and mathematics behind patented Fractional Scaling Digital Signal Processing (FSDSP) filters and algorithms — first introduced in the research and dissertation of Dr. Smigelski, founder of the sNoise Research Laboratory — which are uniquely based on an emerging field of mathematics known as fractional calculus.

Artificial Intelligence 
meets Fractional Calculus

Our goal is to integrate sNRL's patented fractional calculus code-libraries and algorithms into deep learning and artificial intelligence systems, enhancing their performance and pushing the boundaries of what is possible in these rapidly evolving fields. By leveraging the power of fractional calculus combined with AI, we aim to revolutionize anomaly detection, predictive modeling, time series analysis, and other critical areas of data analytics.

Our Market is Extensive

The deep learning and AI market is experiencing exponential growth, with organizations across industries adopting these technologies for improved decision-making, automation, and optimization. However, current algorithms have limitations in capturing long-range dependencies and accurately modeling complex systems. This is where fractional calculus provides a significant advantage, enabling more precise modeling and enhanced performance. Our target market includes AI developers, data scientists, research institutions, and companies looking to harness the full potential of deep learning and FC-AI.

Simplifying the Complex

sNRL is developing code-libraries that integrate our patented fractional calculus DSP algorithms into existing deep learning frameworks and also coding our own frameworks across multiple fields-of-use. These code-libraries will allow practitioners to effortlessly incorporate fractional calculus principles into their models thus lowering the bar of entry to use this advanced technology, improving accuracy, convergence rates, and efficiency. Our libraries will support multiple programming languages and frameworks, ensuring compatibility and ease of use for a wide range of users.

The Future is defined by Fractional Calculus and Artificial Intelligence

sNRL Architects of the Future

Dr. Jeffrey Smigelski, Ph.D.

Founder - CEO - Inventor

Artificial Intelligence

Signal Processing Automata

Timothy Buchenroth

COO - Business Manager

Alex Kong

Software Development

Fractional Calculus 

Core Mathematics Engine 

Charles J. Biederman

General Counsel

When Einstein was asked what was most helpful to him in developing the theory of relativity, he replied, 
“Figuring out how to think about the problem.”

~Albert EinsteiN~