© 2024 sNoise Research Laboratory, LLC.

Fractional Calculus is an

Emerging Scientific Discipline


“…the mathematical models of reality have been pushed to their limits and beyond, those which were developed and applied so successfully to the explanation and understanding of physical phenomena in the nineteenth and twentieth centuries are no longer adequate to describe the emergent phenomena of the twenty-first century. Herein we propose to adopt a fresh perspective entailed by the use of the fractional calculus.”

- Fractional Calculus View of Complexity: Tomorrow’s Science, Bruce J. West, 2016.



Defining Fractional Calculus


The potential scale of applications is enormous.  Think of how big the market is for calculus-based applications.  How much bigger must the market be for Fractional Calculus-based applications?

Fractional Calculus offers unique solutions unavailable to Calculus and is more reflective of our physical reality and how the universe works encoded within digital signals.

Fractional Calculus includes all of Traditional Calculus

Fractional Scaling Digital Signal Processing (FSDSP) performs Fractional Calculus-based signal processing and fractional order control in the complex frequency domain.  Mathematically speaking, Fractional Calculus is a superset of Calculus and Calculus is a subset of Fractional Calculus.


Information -

The Signal in the Noise:

Signals convey information.  In nature, this information may be in the form of an analog or continuous signal, such as a physical sound wave or radio wave.  With proper sampling and sensors, a microphone or radio in this example, we can numerically represent this physical analog wave as a digital signal which is a discrete sequence of numbers that represents all of the information contained within that waveform.

Often when sampling the environment, our sensors collect unwanted signals, disturbances, or extra information that is not relevant to the signal and obscures the information we want to collect.  This irrelevant information we term noise which interferes with our ability to extract meaningful information from the signal and can prevent technologies from working properly. 

Digital Signal Processing

is a Phantom Technology:

Digital Signal Processing (DSP) is often called the Phantom Technology as DSP is in nearly all modern electronics but takes place behind the scenes as algorithms in hardware or software.  More specifically, DSP is applying math encoded as algorithms in hardware or software to those numerical values of a digital signal in order to change the signal in some advantageous or useful way, such as in filtering a signal to extract the useful information as in tuning a digital radio.

As a research, development, and innovation laboratory, the sNoise Research Laboratory (sNRL) is leading the way to develop advanced Fractional Scaling Digital Signal Processing (FSDSP) tools, signal-shaping smart filters, and algorithmic mathematical solutions based on fractional calculus in order to better extract the signal from the noise to reveal the hidden reality of information shaping the technologies of our modern world.

Signal Processing

Removes Noise from the Signal:

In order to decode and clean up a signal that contains noise to make the signal useful, we filter or process the signal through electronic circuits or mathematical algorithms to separate and extract the signal from the noise.  Collectively, cleaning up and filtering a signal to reduce or eliminate noise is one example of Signal Processing.

Whether we realize it or not, everyday, our lives are influenced by signals and shaped by signal processing.  If you drive a car, used navigation maps with GPS, made a cell phone call, used a computer or the Internet, took a digital picture, played a video game, watched a movie on DVD, used a charge card, had a MRI or CAT scan, listened to the radio, or used RADAR or SONAR, you have used signal processing technologies.


What is Signal Processing?


Fractional Order Control Systems

for Dynamic and Emergent Robotics:

As the language of dynamic system models, FSDSP also extends to defining, modeling, and filtering of fractional order control systems (FOCS) and improves the response, stability, and machine learning capability of emergent robotic and AI platforms such as Unmanned Vehicle Systems (UAVs), self-driving vehicles, and satellites.   Fractional Order Control Systems invoking FSDSP, such as a fractional order proportional-integral-derivative (PID) controller, provide greater stability and performance under strong perturbations, are more flexible and better able to adapt to dynamic properties of an environment, have more effective damping characteristics, and may recover faster and with greater accuracy from disturbances.

Applications in Multiple Markets

Industries, and Scientific Disciplines:

By utilizing a fractional calculus approach to digital signal processing, Fractional Scaling Digital Signal Processing and Fractional Scaling Digital Filters provide sNRL the ability 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, all without introducing the mathematical artifacts of current state-of-the-art filters or resulting in a loss of information in the filtered signal.  FSDSP grants sNRL the ability to extract the signal from the noise more effectively with a higher resolution and level of performance than conventional digital signal processing filters and algorithms that are based on traditional integer-based calculus.

Defining Fractional Scaling Digital Signal Processing (FSDSP)

Fractional Scaling

Digital Signal Processing (FSDSP):

Fundamentally, Fractional Scaling Digital Signal Processing (FSDSP) allows fractional calculus, and thus fractional filtering (e.g., fractional scaling, fractional phase shifting, fractional integration, or fractional differentiation) to be performed on a signal.  In fact, FSDSP opens up access to a signal allowing each individual frequency the ability to be adjusted to any decimal level in magnitude and/or phase and also the ability to access or alter the time element of the signal or frequency.  FSDSP also encompasses a new class of digital filters (Fractional Scaling Digital Filters or FSDF) and further defines control systems (Fractional Order Control Systems or FOCS) in terms of Fractional Calculus.  Representing exact filtering solutions rather than approximations, FSDSP demonstrably exhibits extreme mathematical accuracy, precision, robustness, flexibility, and computational efficiency leading to cleaner signals and more effective control.

Defining Fractional Scaling Digital Signal Processing (FSDSP)



Platform Technology:

As the next generation in digital signal processing technology, Fractional Scaling Digital Signal Processing (FSDSP) represents a game-changing platform technology for the digital age, offers the potential to revolutionize the way in which we currently see, model, filter, and control digital signals and systems, and represents a remarkable technological advancement over current digital filter designs.

Multiple Dimensional

Signal Processing:

FSDSP extends to signals across multiple dimensions and may be successfully applied whether that signal is single dimensional as in 1-dimensional arrays (1D time series signals), or a 2-dimensional space (2D static imagery), 3-dimensional space and time (dynamic video or spatio-temporal dynamic texture), or 4-dimensional (2D or 3D plus a time component or spacetime) digital signal.

FSDSP acts like

Fractals for Time:

In one sense, what the discovery of fractals did for shapes and computer-generated imagery, FSDSP does for signals and time. With the Potential to become the industry standard for digital signal processing filters, sNRL's patented FSDSP algorithms may rapidly accelerate technological developments in a variety of fields to generate robust solutions for the future and reveal the true hidden nature of reality.

Simulations - FSDSP is

the mathematics of Nature:

With FSDSP, sNRL can now shape any signal, accessing every frequency in both magnitude and/or phase allowing ultra-realistic simulations, revealing hidden information about recorded and measured quantities of real-world data, electromagnetic, or physical phenomena which may also lead to detailed predictive models or filters of these complex signals and phenomena.


Disruptive and Accessible

Advanced FSDSP Technology:

FSDSP provides a complete, organized mathematical framework and repository of Fractional Calculus encoded as a library of patented algorithms.  sNRL is actively working towards converting the FSDSP algorithms from MATLAB to a multi-language Software Development Kit (SDK) for edge computing and Application Programming Interface (API) for cloud computing with integrated AI and deep learning algorithms.  Custom interfaces and sample data for each field of use and data type are also planned for inclusion into the FSDSP SDK and API so that customers from a variety of scientific and engineering fields may more readily test, license, and implement the fractional calculus algorithms in both hardware and software products leading to widespread adoption potentially disrupting industry standards in many of these disciplines.

Applications in Multiple Markets,

Industries, and Scientific Disciplines:

The usefulness of fractional calculus and FSDSP including Fractional Scaling Digital Filters and their use in fractional order control systems extends across a multitude of disciplines and industries from control theory, cybernetics, economics, information theory, medicine, neuroscience, neuroengineering, cognitive science, and the human behavioral sciences to the environmental sciences, meteorology, geophysics, aerospace, control systems, robotics, mechanical engineering, mechatronics, sensors, electrical engineering, telecommunications, audio, video, and digital signal processing with numerous applications such as RADAR and SONAR Data Acquisition Systems.  Since Fractional Calculus encompasses all of Calculus, the current possibilities and the yet undiscovered potential uses are truly limitless.

Defining Fractional Scaling Digital Signal Processing (FSDSP)

Increase in Number of Published Scientific Articles on FC year to year

Fractional Calculus Integrated into AI is the Future


Increase in Patents Issued for Fractional Calculus (FC) Applications

Computing Power and Sensors in Devices are now catching up to the mathematics

Increased Prevalence of Edge Computing needing Dynamic Digital Signal Processing

A New Market is Emerging for Fractional Calculus-Based Technologies

Fractional Calculus Publications Per Year

sNRL is Uniquely Positioned to Lead the Field



Total Funding to Date: ~   $1M (Sweat Equity, Personal Funding, University Angel Investor) 


20+ years of relevant experience dedicated to research and development in the field 


API (Cloud-based, Internet of Things (IoT)); SDK (Embedded Products, chipsets, Edge Computing)

sNRL Developed and Holds Foundational Patents







All Patents awarded to sNRL to date collectively generate a foundational platform technology enabling Fractional Calculus Algorithms applicable to all data types and signals

IP Protection: These Utility Patents consist of Exclusive IP from University Patents (US and International) and cover the methods and algorithms of Fractional Scaling Digital Signal Processing (FSDSP), Fractional Scaling Digital Filters (FSDF), Fractional Order Control Systems (FOCS), and the modeling, filtering, and generation of Standardized Noise, all of which allow or utilize Fractional Calculus expanding the fields of mathematics and digital signal processing. These algorithms may be further enhanced by inclusion within Artificial Intelligence and Deep Learning applications.

​4 US Patents Issued (2017, 2018, 2019, and 2020): U.S. Patent 9,740,662, Issued 22AUG2017; U.S. Patent 10,164,609B2, Issued 25DEC2018; U.S. Patent 10,169,293B2, Issued 01JAN2019; U.S. Patent 10,727,813B2, Issued 28JUL2020.

Additional algorithms enhancing the issued patents are already in development and ready to file as provisional patents.


FSDSP is the Next Generation of Digital Signal Processing

Access to Fractional Calculus is Central

to the Evolution of Artificial Intelligence


Fractional Calculus and Artificial Intelligence


Artificial Intelligence
meets Fractional Calculus:

sNRL aims to revolutionize the deep learning and AI industry by incorporating fractional calculus into code-libraries tailored for machine-learning and deep-learning applications.  While there are libraries and frameworks available for deep learning and AI, no existing solutions specifically focus on integrating fractional calculus algorithms into AI which would effectively change the way AI "thinks" by giving AI a larger "superset" of mathematical tools.


Foothold Technology:

​Fractional Calculus AI presents a unique opportunity for sNRL to establish a strong foothold in the market across the increasingly competitive landscape of deep learning and AI.  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 to drive innovation.

Improve Existing AI;

Invent Future AI:

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.  ​​sNRL is both developing code-libraries that integrate our patented fractional calculus DSP algorithms into existing deep learning AI frameworks and also coding our own Fractional Calculus AI frameworks (FC-AI) across multiple fields-of-use.

AI Access to

Fractional Calculus:

These FSDSP and fractional calculus AI 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, solution convergence rates, and efficiency of calculations. Our libraries will support multiple programming languages and frameworks, ensuring compatibility and ease of use for a wide range of users.

​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 (FC) analytical tools which include advanced digital signal processing libraries, signal-shaping smart filters, advanced machine/deep-learning toolboxes made for AI, 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.

Fractional Calculus and Artificial Intelligence


Breaking Limitations
of an Exponential Market:

​​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.


the Complex:

By building fractional calculus code toolboxes for AI, sNRL is providing AI an expanded mathematical library leading to more advanced solutions and the ability to tackle the toughest problems in signal processing. Additionally, the equations of FSDSP are computationally more efficient and can save energy, battery life, and memory used by AI. Our target market includes AI developers, data scientists, research institutions, government, and companies looking to harness the full potential of deep learning and FC-AI.

A New Era

of Intelligent Systems:

By investing in sNRL today, you will be supporting a startup at the forefront of innovation at the intersection of fractional calculus, deep learning, and AI. Together, we will redefine the boundaries of what is possible and pave the way for a new era of intelligent systems. Our patented algorithms and code-libraries have the potential to transform industries across the board, from finance and healthcare to robotics and autonomous systems.

A New Frontier

in Signal Processing:

Through key strategic partnerships and investments, sNRL will better be able to accelerate the development and deployment of our fractional calculus code-libraries enhanced with deep learning capabilities. By joining forces with sNRL, you will be part of an incredible journey to reshape the future of deep learning and AI, unlocking new frontiers in data analysis, modeling, and driving groundbreaking advancements.

Our proprietary algorithms, protected by patents, provide a solid foundation and significant competitive advantage, ensuring the exclusivity and precedence of our cutting-edge technology. We also prioritize continuous research and development, staying ahead of competitors by consistently providing innovative solutions.  To use an analogy, sNRL is building the "ChatGPT" of digital signal processing through fractional calculus embedded within AI (FC-AI) that may identify, amplify, attenuate, filter, reconstruct, denoise, or synthesize any numerical signal with structure.

The Market is at the Threshold of Exponential Growth

\frac {dN}{dt} = rN(\frac{K-N}{K})

Applications that were once only in the lab now have massive commercial applications, especially when Fractional Calculus is Integrated into Artificial Intelligence Systems.

Fractional Calculus, although first introduced 300 years ago, is an emerging field of mathematics and applied scientific discipline which has accelerated in this past decade as computing power and sensor technologies have caught up with the mathematics.  Fractional Calculus has vast and extensive applications in all related fields of science and engineering.

21st century problems


21st century solutions


sNRL SDK, API, and FSDSP Chipset with Integrated AI

Application Programming Interface (API)

  • Cloud-based Fractional Calculus AI
  • Data Server and FSDSP Processor
  • Internet of Things (IoT)
  • The "ChatGPT" of Signal Processing

Software Development Kit (SDK)

  • Embedded Products
  • Edge Computing
  • Microcontrollers
  • FSDSP Chipsets

Fractional Scaling Digital Signal Processing (FSDSP) Chip/Microcontroller

  • Incorporates FSDSP/Fractional Calculus
  • May be programmed through SDK
  • May interface with API

Licensing Vehicle

AI Engine/FSDSP Toolkits



Write Once...

Run Anywhere:

Expect nonlinear or exponential response out of proportion to our entry into market.  Once patented algorithms with AI are integrated into an API and SDK, codebase may be run on multiple types of datasets or deployed via multiple types of microcontrollers or chipsets. Design and maintenance of front-ends for specific fields of use ensure compatibility. Additional feature sets, analysis, and use cases continuously being developed and patented.


Algorithm Search Engine:

Currently, one cannot just Google an algorithm for quick implementation. The sNRLTECH codebase provides a search engine for standardized algorithms and fractional order control system applications.  Essentially, the API and SDK provide plug and play capability for sensors to use advanced digital signal processing combined with AI for rapid incorporation into prototypes and licensing of any proprietary algorithms.

Clearinghouse for

Algorithmic-based Tech:

Although focused on Fractional Calculus and AI, the API and SDK may become a searchable database of proprietary algorithms that may be licensed with a percent of the licensing fee to sNRL for maintaining and licensing these algorithms.  This would also create a market for algorithms at Universities that otherwise go unlicensed making them more accessible. Scientists could add their own proprietary data or algorithms to the database and share in licensing fees.

Modularized Building

Block Architecture:

The sNRLTECH API and SDK creates an architectural framework of modularized building block algorithms that seamlessly link together so that any signal is processed correctly with minimal effort.  An import tool utilizing deep learning and AI would serve to orientate the input data correctly for the algorithms based on the type of data to be processed.  Rapid generation of FSDSP filters are obtained through a Data Equalizer GUI.

sNRLTECH: A fractional scaling digital signal processing SDK, API, chipset integration, user-interface, and licensing platform utilizing sNRL's patented advanced mathematical algorithms based on fractional calculus further enhanced through deep learning and AI to filter, model, reconstruct, and synthesize digital signals from a variety of data measurements and sources such as audio, radio, video, industrial, SONAR, RADAR, and medical sensors.


sNRL SDK, API, and FSDSP Chipset with Integrated AI

Licensing Vehicle: sNRLTECH.com

  • Enterprise SDK and API with FC-AI Engine 
  • Application-centric Microservices
  • Multiple Fields of Use/Front-ends per data type
  • Multiple License Types
  • Hosted AWS cloud for scalability
  • Can be loaded onto hardware or embedded in chips
  • Provides automatic sign-up, billing, license checkout
  • Dedicated User-Based Community and Marketplace

Lowers the bar of entry for anyone to use the power of fractional calculus and AI from the student to the advanced signals processing engineer, mathematician, or scientist (e.g., data equalizer)

Provides a Vehicle to License patented FSDSP technology enhanced with AI to a variety of industries and for use embedded in products automatically setting up licenses and billing according to use

Provides metrics of use and scaling so that licenses may be monitored and appropriately priced per data processed, field of use, and/or embedded product or FSDSP microcontroller or FSDSP Chipset

Potential to also create dedicated user-based community in which scientists, engineers, and programmers may develop add-ons, specific front-ends for abstract data sets, or share data and settings driving engagement and licensing of sNRL technology


Validation and Proof-Of-Concept Confirmations


Electromagnetic Borehole Telemetry

FSDSP provides a solution to solve the problem of in-band noise in an Electromagnetic (EM) Borehole Telemetry System of land-based drill rigs that provide EM telemetry from the smart drill in the borehole to the drill rig at the surface using extremely low frequencies (<=12Hz).  Of note, all land-based rigs with EM Borehole Telemetry systems have the same in-band noise problems so the market extends to all operators of such rigs and also Ground-Penetrating RADAR systems.

Evaluation of FSDSP by Engineers at an Oil Field Services Company that supplied the data showed that FSDSP did increase the signal-to-noise ratio (SNR), accuracy of symbol decoding, and recognition of EM Borehole Telemetry data when compared to the traditional digital signal processing methods currently used by the same Oil Field Services Company.

The increased accuracy of FSDSP will significantly reduce re-boring (at a estimated cost of $1-2m per borehole, on average) leading to more efficient operations.

As a result of this validation and proof-of-concept application of FSDSP to telemetry data, the management of the Oil Field Services Company requested an outline of licensing terms of Fractional Scaling Digital Signal Processing from sNRL for a Multi-Year, Multi-Use License with Oil Field Exclusivity.  Primarily, with this license, sNRL's FSDSP technology would be utilized through the API and SDK to improve upon current Electromagnetic Borehole Telemetry signal processing in land-based drill rigs.


Audio/Data Signal Processing x100

Multiple Music and Audio Signal Processing Companies and Advanced Military Technology Developers have expressed interest in licensing the FSDSP SDK or API once developed

Audio Signal Processing Applications

  • Voice Synthesis, Extraction, Compression, and Conversion
  • Music and Instrument Synthesis and Hybridization, Speaker Emulation
  • Noise Reduction, Isolation, Selective Removal of Individual Frequencies
  • 3D Audio, Binaural Audio, Directional Filtering, Directional Enhancement
  • Enhanced or Augmented Reality Hearing, OTC Hearing Aids, Hearing Emulation

FSDSP allows for unparalleled audio signal processing capabilities with the ability to access and adjust every single individual frequency, in both magnitude and/or phase, and time element of each frequency (i.e.,  forward, backward, or pausing individual frequencies) without affecting or interfering with adjacent frequencies.


RADAR/SONAR Signal Processing x100

SONAR and RADAR processing applications are immense

  • Potential to develop new Synthetic Aperture RADAR (SAR) ultra-high resolution FSDSP Doppler Filter Banks
  • Improved Resolution of Side-Scan and Multi-beam SONAR Imagery
  • Improved SNR and Clutter Suppression of Noise in RADAR and SONAR
  • Enhancement of Individual Frequencies revealing otherwise Stealth Signatures
  • Applications: Aircraft, Autonomous Vehicles, Drones, Weather, Satellites, Shipboard, Sensors, and more

High Value Medical and Telemedicine Applications

FSDSP allows for the digitization of brain and nervous system signals through the development of advanced signal processing algorithms for a Brain-Machine Interface or bioelectrical interface. For example, FSDSP offers potential enhancements of the bioelectrical interface stemming from improvements in sensor and filter design which may yield significant increases the functionality of bionic neuroprosthetics. This allows for more accurate identification and interpretation of bioelectrical signals, such as augmenting the signal passed through a spinal cord bridge to repair paralysis. Additionally, FSDSP allows for EEG brain wave isolation and extraction, analysis, and identification/modeling of brain and nervous system activity leading to highly accurate mathematical, computationally-based methods to quantitatively diagnose neurological disorders.

FSDSP provides the capability to address characteristics of human information processing, enhance the collection, analysis, and filtering of brain, electrical, and nervous system signals, and allows for the development of more accurate and advanced sensors used to record electrical activity, such as EEG data, through enhanced filtering equations.

Sample of FSDSP Medical Applications:

  • Spectroscopy
  • Speech Pathology
  • Biomedical Imaging (MRI)
  • Genetics
  • Neuroscience / Neuroengineering


  • Robotics / Bionics
  • First Responders
  • Telemedicine / Sensors
  • Directed Energy Therapy
  • Cardiology

EKG Heart Rate, Breathing Extraction, and Analysis from Video

Original Video*

FSDSP Video*

* Original Video - MIT Labs; * FSDSP Video - sNoise Research Laboratory


FSDSP Software-Defined-Radio to solve Electromagnetic Spectrum Access

FSDSP offers a mathematical solution to increase spectrum capacity to address the Spectrum Congestion Problem, allowing both more usable frequency bands (more subchannels) per allocated bandwidth and also more devices per channel (more frequencies available to hop between).  FSDSP can be used to create extremely sharp digital filters that can isolate bands of frequencies without interfering with and independent of adjacent frequencies (e.g., HD multiplexing).

FSDSP offers the potential to increase spectrum capacity by more efficiently splitting up access to the physical spectrum through FSDSP filters. Thus, more information, stations, or channels may be added to current Spectrum Allocations without loss of fidelity resulting in an increase in the performance of radio digital signal applications or related devices ranging from Software-Defined Radio (SDR), Fiber Optics, IoT, and telecommunications, to Bluetooth, Wi-Fi, and video (e.g., streaming) or radio broadcasting.

FCC Narrowbanding Mandate - Traditional Filters

FCC Narrowbanding Mandate - FSDSP Filters

With improved fractional scaling digital filters for tuning and isolating a signal from the interference of adjacent frequencies, FSDSP extends the usable reception range leading to fewer dropped calls and less noise in radio signals.

Encryption of Radio Signals within Noise (Military-Tactical Software Defined Radios) is also possible through FSDSP offering the ability to embed high resolution signals in what appears to be random noise or any other signal over open airwaves.



FSDSP, utilizing Fractional Calculus, provides an extensive mathematical toolset to enhance Machine/Deep Learning (ML) and Artificial Intelligence (AI) Algorithms for Feature/Pattern Detection, Extraction, and Synthesis/Reconstruction of a signal.

FSDSP captures the scaling and frequency behavior of complex time series, systems, and structured data within equations and can yield statistically identical simulations and models of these data sets to allow you to "animate", extrapolate, filter, simulate, and understand these systems giving us the ability to determine "What’s inside the black box?" and how the ML algorithms or AI came to a solution.

FSDSP also allows for the development of more efficient and accurate ML algorithms for signal processing as there are fewer parameters necessary within the FSDSP equations that would need to be modified or modeled by a ML system when compared to traditional filters and systems allowing AI to come to a solution faster yet more accurately.

This is why FSDSP can emulate natural signals such as a realistic voice as the equations of FSDSP and mathematics of Fractional Calculus more accurately represent how nature works and may bring us closer to a true, cognitively functioning AI.


A $30 M Investment Provides sNRL Multiple Runways Over 5 Years

Paths to Markets

A $30 million dollar investment provides sNRL with multiple runways over 5 years at an estimated $6M burn rate per year employing 33 people at the Institute with the possibility to begin generating revenue within the first year.  Multiple projects, in distinct fields of use, may be started at the same time, each potentially generating revenue within a year.  This investment also allows us to secure a building for the Fractional Calculus and AI Institute.

Once the main FSDSP SDK, API, and hardware solutions are built, they can be applied to multiple fields of use with limited changes to front and back end designs for specific data types and formats.  The core FSDSP and FC-AI algorithms remain intact.

As such, a faster path to market is available for the roll-out of products for all fields of use after development of the initial fractional calculus SDK/API and AI code libraries.


Seeking Accredited Investment Partner to Level Up Humanity

Building the Algorithms that Shape Our World

The TIME is NOW:

Emerging Fields in Frontier Science:

Fractional Calculus is emerging as a significant field of mathematics while Artificial Intelligence is experiencing exponential growth, both of which have profound applications in all related fields of science and engineering. Only recently in the past 15 years have researchers and academics really begun to explore Fractional Calculus as an applied discipline, as advancements in computer processing power and sensor technologies have made possible the handling of the breadth of calculations which had previously precluded any serious application of the subject.

Advances in Processors have Opened

the Door to Breakthrough Innovation:

Likewise, these same advances in computer processing power have led to an explosion in Artificial Intelligence technologies, with new discoveries occurring on an almost daily basis.  Still, while AI is thought of as the next frontier, AI is still in its early stages of development. Furthermore, dedicated research and development of Fractional Calculus currently exists only in a few rarefied universities and laboratories.

In order to push the envelope of Frontier Science, AI must be given access to the power, accuracy, robustness, and efficiency of fractional calculus.


Fractional Calculus and Artificial Intelligence Institute:

R&D Think Tank and Educational Institute


Fractional Calculus and Artificial Intelligence Institute:

R&D Think Tank and Educational Institute

Together, with your financial support, mentorship, and partnership, we can serve as the cornerstone for the foundation of the Fractional Calculus and Artificial Intelligence Institute - something the world has never seen before - and lead the way bringing Fractional Calculus and AI to the forefront of this next frontier of science and mathematics, laying the foundation for future technologies to level up humanity.  We believe that the establishment of the Fractional Calculus and Artificial Intelligence Institute will become a Bifurcation Event in the history of science and mathematics as humanity embarks on a new trajectory of knowledge and understanding of the world around us.

A crucial step for the future is to consolidate the groundbreaking work we have done to push the development and adoption of the power of Fractional Calculus and integrate this expansive mathematics into deep learning and AI to spawn the next scientific revolution.

We propose to establish a first of its kind Fractional Calculus and Artificial Intelligence Institute which would serve as a think tank and interdisciplinary research laboratory of scientists, mathematicians, computer programmers, doctors, and students. We expect the Fractional Calculus and Artificial Intelligence Institute to become a leader in the development of the foundational mathematics and algorithms of Fractional Calculus and AI in multiple fields of science and engineering including aerospace, automotive, robotics, energy, and medicine to achieve breakthrough technological advancements with beyond state-of-the-art performance.

Personnel of the R&D lab of sNRL form a core group to serve as a basis to establish a "first-of-its-kind" Fractional Calculus and Artificial Intelligence Institute.

  • Develop Fractional Calculus toolbox libraries tailored for machine-learning and deep-learning applications merging Artificial Intelligence with the advanced digital signal processing capabilities of fractional calculus across a variety of fields-of-use within current and future technologies
  • To perform educational outreach, host visiting scholars and scientists, and develop seminars, courses, and educational materials such as textbooks to teach fractional calculus since the mathematics of FC is currently not taught in schools or universities unless part of a specialized curriculum and to educate on the science and promise that deep learning and AI hold for the future.
  • To perform basic and advanced research for the continued development of the fields of fractional calculus mathematics and artificial intelligence, their applications, and integration of FC into AI/DL and future technologies such as quantum computers and oscillator-based computers (OBCs)
  • To serve as a Clearinghouse for establishing the foundational mathematics and algorithms of Fractional Calculus and their implementation into AI algorithms across a variety of fields of use and scientific disciplines
  • To work with universities, laboratories, industry, and governmental agencies collaborating on research projects and technological development to propel the fields of FC and AI forward

Core functions of the

Fractional Calculus and Artificial Intelligence Institute include:

Fractional Calculus and Artificial Intelligence Institute:

R&D Think Tank and Educational Institute

The Institute in conjunction with sNRL would also serve to:

  • Research and identify legacy technology upgrades and new-use cases to add to the knowledge-base of fractional calculus applications and explore enhancement by AI
  • Assist sNRL in practical implementation of identified upgrades and FC-AI applications
  • Create a repository of FC and AI algorithms and a platform for easy integration of algorithm libraries developed at the institute for licensing by sNRL through sNRLTECH
  • Guide development of Licensable Software Development Kits (FSDSP SDK), API, and FSDSP Chipsets that use FC-AI to be geared towards specific types of data sets, fields-of-use, or applications and provide a laboratory test bed for these SDK, API, and Chipsets
  • Provide a vehicle for joint development agreements with scientists, companies, industries, or governmental organizations or agencies that wish to utilize our FC-AI technology


sNRL then may also further serve to fund, protect, patent, license, and commercialize technologies developed by the Fractional Calculus and Artificial Intelligence Institute which provide both sNRL and the inventor protection and a pathway to market for one’s work.

The Fractional Calculus and Artificial Intelligence Institute expands the Research and Development capabilities of sNRL

Fractional Calculus and Artificial Intelligence Institute:

R&D Think Tank and Educational Institute


Meet the Core Team

We welcome you

to join our team!

The fractional.

“The difference between ordinary and extraordinary is the little extra”.