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A New DSP Paradigm
DSP and IFR
IFR's Mathematical Advantage
IFR vs. Fourier Transforms
Applications of IFR Technology
Expanded Features & Benefits Chart
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Applying IFR Menu
DSP and IFR Back to Table of Contents
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Until now, Digital Signal Processing (DSP) has had to rely on Fourier transforms for mathematical analysis of sensory data. Fourier transforms are the equations of choice for DSP because they can receive any signal coming into the computer and consistently yield some limited, but reliable information. The problem with Fourier transforms is that they discard much of the signal's data, rendering a corrupted translation.

Colorcom's proprietary IFR technology has made Fourier transforms obsolete in most DSP applications. Similar to Fourier transforms, IFR can handle any signal coming into the computer; however, IFR far surpasses Fourier transforms in signal detection, analysis and information extraction.

IFR's Mathematical Advantage Back to Table of Contents
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Fourier transforms are limited to a spectrum analysis of the frequencies of signals. Colorcom's powerful new raster to vector converter technology represents the signal in a continuous mathematical representation. IFR's abstract hierarchical representation flows seamlessly across equations. The end of one equation transforms into the beginning of the next equation, with no loss of raw data. This allows many mathematical algorithms to be applied to DSP for signal detection, feature extraction and data interpretation.
IFR vs. Fourier Transforms Back to Table of Contents
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IFR represents any incoming signal in seamless mathematical formulas, which allows the extraction of many artifacts from a single signal. This ongoing extraction of information means:
  • All data are converted to equations in a lossless fashion.
  • Super-imposition is avoided.
  • The beginning and end points of a specified signal can be clearly and precisely identified.
  • Any specified signal can be resolved, even if it transverses several native equations, as these equations can be translated to a more application-appropriate set if necessary.
  • IFR's non-recursive extraction of equations from signals often allows for less expensive hardware implementation.
  • IFR's mathematical representations allow the data to be sorted and categorized in a multitude of ways. Therefore, a database of information can be built automatically.
  • IFR allows secondary mathematical manipulations, such as the correlation of signals to a database of possible matches.
  • IFR exceeds the capabilities of fractal and wavelet based signal analyses because of their limited view of true signal shape and size.
Applications of IFR Technology Back to Table of Contents
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IFR technology will have a tremendous impact on DSP technology in the following areas: 
  • Wireless Communications
  • Medical Monitoring
  • Disk Controllers
  • Motor Controllers
  • Radar
  • Modems
  • Automotive
  • Architecture
  • Speech Synthesis
  • Music Synthesis
Expanded Features & Benefits Chart Back to Table of Contents
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Application
Features
Benefits
Wireless Communications IFR technology enhances the signal-to-noise ratio in RF transmissions by a factor of 100. Since IFR is not tied into Fourier transforms, it can use different equations and abstractions such as correlation of equations to a database of candidate matches. IFR improves every wireless product that communicates via RF and must extract signals from noise with:
     
  • Exact sound reproduction 
  • Compressed file size = 800:1 
  • Bandwidth efficiency increased 10,000:1
Medical Monitoring IFR's enhanced signal-to-noise ratio improves clarity of medical images and allows precision monitoring. IFR improves: 
  • Respiratory monitors 
  • Heart monitors 
  • Oxygen and new non-invasive blood monitors 
  • Ultrasound images
Disk Controllers At high disk speeds, IFR extracts signals more efficiently. Noise matters less because there are more tools, not just simple filters. Since it is not limited to the Fourier transform, IFR can use a number of different calculations. IFR delivers: 
  • 100 times more storage capacity and rapid data extraction 
  • Higher capacity CD-ROMs 
  • Higher performance disk controllers
Motor
Controllers		 IFR analyzes signals using proprietary mathematical algorithms rather than more overhead intensive Fourier transforms. IFR technology provides: 
  • Less overhead 
  • On-the-fly optimization rather than over-time optimization by Fourier transforms
  • Faster data stream
Radar IFR enhances the signal-to-noise ratio by a factor of 100. Filters are not used.  IFR improves radar sensing: 
  • Excitation signals can be changed more rapidly because filtering is
    less of a factor
  • Incoming signals are not convoluted by the filter resonance 
  • Allows a more precise subtraction of the excitation signal 
  • Enhances the data left from the field of view
  • More complex excitation signals can be used -- allows for more efficient
    data extraction
Modems IFR does not use filtering to extract signals, so bandwidth is used efficiently. IFR allows signal complexities to be added to carry additional information within the same bandwidth. IFR allows detection of small signal differences. IFR enhances modems by: 
  • Increased speed over existing phone lines
  • Increased lossless graphic compression
Automotive IFR collects more and superior data, efficiently detecting noise from signals on-the-fly. IFR optimizes automotive performance by enabling: 
  • Speed and load analysis
  • Automated carburetor/fuel injection adjustments
  • Early detection of warning noises, comparison to a database to determine need for repair
  • Information extracted from the data stream faster 
  • Allows on-the-fly corrections and adjustments
Architecture IFR makes it possible to take refined temperature and motion measurements to make constant comparisons, even over long periods of time. IFR can: 
  • Determine the stability of a building based on its movement relative to temperature, over long periods of time. This is more straightforward than current DSPs because correlation, not frequency, is the determining factor.
Speech Synthesis Fourier transform filters do not extract enough information from sound signals to obtain human-like speech recognition. IFR allows flexible mathematical equations to be applied, making speech recognition and synthesis possible. IFR provides real time speech analysis/synthesis and:
  • Human-like speech recognition
  • Identification, isolate and replay of individual voices
  • Accurate, robust speech recognition over various voices, including new voices
  • Enables voice identification - a key to secure online commerce
  • Allows speech recognition to replace current security and data entry methods
Music Synthesis IFR analyzes music in audio sprites, capturing and reproducing exact sounds in smaller sound files. IFR enhances music production and reproduction: 
  • Allowing voices/notes to be key-corrected 
  • Voices/notes can be replayed in series through different instruments 
  • Conserving the voice dramatization of a particular singer's or instrument's personality
  • Reducing file sizes for storage and transmission
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