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.

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.

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.

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.

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.

Next-Generation

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.

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.

sNRL seeks Partnerships to bring

Fractional Calculus to the World:

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.

BOOTSTRAPPED BY FOUNDERS

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

EXPERIENCED VISIONARY LEADERSHIP

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

2016

2017

2018

2019

2020

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.

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.

Foundational,

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.

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.

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.

Simplifying

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.

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.

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.

Standardized

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.

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.

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.

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.

Integration of Fractional Calculus

Into Deep Learning and AI Algorithms:

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