Fractional Scaling Digital Signal Processing is the foundation of our business, and allows us to provide a variety of services. Find out how we can help you achieve breakthrough performance with FSDSP.
What is 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 based on Fractional Calculus that enable products and services to achieve breakthrough performance.
sNRL’s advanced, patented Fractional Scaling Digital Signal Processing algorithms let you quantitatively define and shape the spectrum of any signal. Fractional Scaling Digital Signal Processing (FSDSP) uses fractional calculus, affording fractional filtering (e.g., fractional scaling, fractional phase shifting, fractional integration, or fractional differentiation). This represents more exact filtering solutions, not approximations, and demonstrably is more accurate, efficient, and performs better than conventional DSP filters.
Industries We Serve
The concepts of Fractional Scaling Digital Signal Processing, encompassing Fractional Scaling Digital Filters and their use in fractional order control systems, extends across a multitude of disciplines and industries.
- Control theory
- Cybernetics
- Information theory
- Medicine
- Neuroscience
- Neuroengineering
- Cognitive science
- Human behavioral sciences
- Environmental sciences
- Meteorology
- Geophysics
- Aerospace
- Control systems
- Robotics
- Mechanical engineering
- Mechatronics
- Sensors
- Electrical engineering
- Telecommunications
- Audio
- Video
- Digital signal processing
...with numerous applications such as RADAR and SONAR Data Acquisition Systems.
Applications of sNRL
As the next generation in digital filter technology, Fractional Scaling Digital Filters may be used in FSDSP of any digital signal that represents information about a recorded, measured quantity of real-world electrical or physical phenomenon that varies with time (or position) such as, but not limited to:
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Audio (e.g., sound, music, and speech)Communications
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Radio
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Video
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Television
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Voltage
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Current
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Pressure
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Imagery
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Medical data (e.g., EEG, MEG, fMRI data)
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Economic data (e.g., stock market prices)
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Environmental data (e.g., water level fluctuations, temperature)
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Positional data (e.g., SONAR, RADAR, GPS or other sensor measurements as input to fractional order control systems or filtered utilizing Fractional Scaling Digital Filters).
Benefits of FSDSP
Shaping Spectrums
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.
Exact Decimal/Fractional Decibel Levels
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 stateof-the-art filters.
Scalable
FSDSP is scalable and can be encoded into a field-programmable gate array (FPGA) device or within a DSP chipset. Compared to conventional DSP, FSDSP requires fewer equations, parameters, and steps to achieve fractional rates of attenuation or amplification of specific frequency regions translating into a reduction in the amount of time necessary for calculations, less error propagation, a reduction in the memory necessary for the calculations, and a reduction in the energy per operation.
Machine/Deep Learning
FSDSP may be applied to Machine/Deep Learning for Feature/Pattern Detection, Extraction, and Synthesis. FSDSP captures the scaling behavior of complex time series, systems, and structured data within equations and can yield statistically identical simulations of these data sets.
Improvement Capabilities
FSDSP improves the response, stability, and machine learning capability of robotic platforms such as UAVs. 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.
Accurate and Realistic Computer Models
FSDSP can produce more accurate and realistic computer models and simulations of natural environments through computer-generated 2D imagery and 3D textures
Easily Filter Distinct Frequencies
FSDSP provides a method to easily filter out or amplify distinct frequencies or ranges of frequencies utilizing a fractional equalizer and may be applied in near-real time to live data streams from IoT.
Clean Up Signals
FSDSP provides methods to clean up signals and increase signal-to-noise ratio from ubiquitous and noisy sensor/actuators that live at the edge of the network in IoT.