Services

The sNoise Research Laboratory (sNRL) is a research, development, and innovation laboratory focused on the science of noise (sNoise®) and the corresponding technology applications.  

sNoise®, a registered trademark of sNRL, is the science and mathematics behind our patented Fractional Scaling Digital Signal Processing (FSDSP) — a remarkable technological advancement and game-changing platform technology for the digital age — first introduced in the dissertation of Dr. Smigelski, founder of sNRL.  

sNRL, through the application of the fractional calculus of sNoise® algorithms, as FSDSP libraries embedded in an API and SDK, expects to rapidly accelerate technological developments in a variety of fields — including audio (e.g., sound, music, and speech), video (e.g., augmented and virtual reality), medical (e.g., medical devices and imagery), mechatronics (e.g., automotive, aerospace, defense systems, and manufacturing), robotics (e.g., fractional order control systems), artificial intelligence (deep learning and machine learning), and more — to generate robust solutions for the future.  

sNRL strives to be the positive change that we wish to see in the world to create a better future becoming the standard in the digital signal processing industry and drive the evolution of AI.

Focus Areas

sNRL can help you achieve revolutionary innovation through the application of sNoise® science and research to both current and new technologies within three key focus areas:

Products

Including enhanced sensors, fractional calculus-based software algorithmic libraries, digital "smart" filters, chipsets, microcontrollers, Fractional Order Control Systems, software-as-a-service (SaaS).

Research

Focused on the conversion of existing and legacy technologies and development of new technologies such as fractional calculus-based artificial intelligence.

Realistic Simulations

To discover and model the internal dynamics of natural or physical systems through a quantitative, equation-based approach using novel mathematical methods, computation experiments, and fractional calculus digital signal processing techniques to better understand and illuminate the world in which we live.

sNRL's Patented Algorithms: Fractional Scaling Digital Signal Processing

The sNoise Research Laboratory developed and patented our innovative Fractional Scaling Digital Signal Processing (FSDSP) algorithms as a go-to, foundational, platform technology for the digital age. FSDSP allows us to develop digital signal processing (DSP) algorithms and libraries based on Fractional Calculus that enable products and services to achieve breakthrough performance.

What It Is

sNRL uses a form a fractional Calculus...an emerging field of mathematics and sNRL invented the mathematics of Fractional Scaling Digital Signal Processing.  sNRL’s patented Advanced Fractional Scaling Digital Signal Processing algorithms let you quantitatively define and shape the spectrum of any signal. FSDSP allows specific fractional scaling equations to be written for the spectrum of any signal or sound. The equations can then be used in algorithms as filters to shape or recognize the spectrum or as models to simulate or reproduce the spectrum.

Why It's Better

In other words, FSDSP allows exact decimal/fractional decibel levels of attenuation or amplification at each frequency to selectively filter complex data sets and can achieve nearly any desired filtering characteristic with a high degree of accuracy, from sharp transitions within a narrow bandwidth to complicated structures within the passband. This prevents introduction of mathematical artifacts or loss of information from the filtered signal, as commonly happens with current state-of-the-art filters.

Benefits
of FSDSP

sNRL's advanced, patented Fractional Scaling Digital Signal Processing algorithms let you quantitatively define and shape the spectrum of any signal. The possibilities for applications are endless​.

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"Profound study of nature is 
the most fertile source of mathematical discoveries."

Jean Baptiste Joseph Fourier 
Théorie analytique de la chaleur 
1822