Proof of concept, Resolution, Future Advances in resolution, Speed, Calibration
Resolution
Identification (Resolution) Theory
The newly patented hardware-based, Digital Tunable Filter (7,529,404) shown below (Figure 1), is posed to achieve NASA’s aim in exploration of outer space stars and finding an Earth-like planet much quicker and with far less cost. The operation of the Digital Tunable Filter is very similar to our daily life of watching T.V. It behaves the same way as our brain identification of targets. When we watch T.V. our eyes detects primary colors of a pixel (or group of pixels) and our brain compares the detected colors to a pre-recorded colors (in the brain), on the basis of association. The exception is that the technology does not to pre-load trillions of different shades of color. There are no computation for filtering and identification.
The newly patented hardware-based, Digital Tunable Filter (7,529,404) shown below (Figure 1), is posed to achieve NASA’s aim in exploration of outer space stars and finding an Earth-like planet much quicker and with far less cost. The operation of the Digital Tunable Filter is very similar to our daily life of watching T.V. It behaves the same way as our brain identification of targets. When we watch T.V. our eyes detects primary colors of a pixel (or group of pixels) and our brain compares the detected colors to a pre-recorded colors (in the brain), on the basis of association. The exception is that the technology does not to pre-load trillions of different shades of color. There are no computation for filtering and identification.
Differences in Resolutions Compared with Current DSP Methods
The proposed technology of pixel by pixel filtering is new thus it is hard to find literature to compare the new methodology of filtering and identification to the ongoing methods. Without resorting to a high level of technical comparisons of this Tunable Filter methodology and the present DSP practices (in which itself is a big task). The present tech Kalman filters, correlation filters and Fast Fourier Transforms (FFT) are currently used to filter and isolate a target from surrounding noise [4, 5]. They have widespread use in pattern recognition, class or cluster recognition in which variety of attributes of a moving object is used for identification and tracking. The filtering and detection is on the cluster bases [13] or motion attributes like velocity [12] or accelerations.
The proposed technology of pixel by pixel filtering is new thus it is hard to find literature to compare the new methodology of filtering and identification to the ongoing methods. Without resorting to a high level of technical comparisons of this Tunable Filter methodology and the present DSP practices (in which itself is a big task). The present tech Kalman filters, correlation filters and Fast Fourier Transforms (FFT) are currently used to filter and isolate a target from surrounding noise [4, 5]. They have widespread use in pattern recognition, class or cluster recognition in which variety of attributes of a moving object is used for identification and tracking. The filtering and detection is on the cluster bases [13] or motion attributes like velocity [12] or accelerations.
Future Advancements
The power behind differentiations of colors is based upon probability theory. When we throw a dice, the probability of getting any number (1 to 6) is 1/6. For three dices, the probability is 1/6^3 = 1/216. The same methodology applies for greater resolutions in which the number of dices is represented by number of channels (or primes) and the number of dots on a face of the dice is represented by number of bits of the A/D converter. Thus the number of shades of color of a single pixel (resolutions in visible light) is given by:
[1/256^3] ^2 = 1/256^6 = 1/281,474,976,710,656. = 2.81* 10^14.
With four primes and 9 bits of the A/D converter, the odds are 1/ 512 ^4 = 1/ 68,719,476,736. This is 4,096 times more power of resolution. For four primes and 12 bits of the A/D converter, the resolution is 1/281,474,976,710,656 or 16,777,216 times more power of resolution compared to the three primes and 8 bit A/D converter. For two or more pixels the resolution increases exponentially to much greater odds (equation 2).
Note: The above detection Figures of a star by the simulator, is based on 3 primes and 8 bit per prime.
The Achievable four primes and 12 bit per prime provides brightness resolutions of magnitudes of 10^14 better than 10^10 desired by NASA. This technology provides 10^4 or 10,000 time better differentiations in brightness. To separate planets from stars, the combinations of visible light and IR should provide the masking (filter) that is required to detect an Earth like planet surrounded by water. The IR signature of H2O is quite distinct. For a four channel of IR and 12 bits per A/D converter, the equation for resolutions is:
This provides resolutions of 1/281,474,976,710,656 * 281,474,976,710,656 = 1/ 2.8 * 10^28.
For two or more pixels the resolution increases exponentially to much greater odds given by.
This technology is posed to resolve the masking and starlight suppression problems economically in shorter time.
The power behind differentiations of colors is based upon probability theory. When we throw a dice, the probability of getting any number (1 to 6) is 1/6. For three dices, the probability is 1/6^3 = 1/216. The same methodology applies for greater resolutions in which the number of dices is represented by number of channels (or primes) and the number of dots on a face of the dice is represented by number of bits of the A/D converter. Thus the number of shades of color of a single pixel (resolutions in visible light) is given by:
- Probability of Detection of shade of color o a pixel = 1/d^p. Equation 1
- Probability of identification with ‘n’ number of pixels = [1/d^p]^n. Equation 2
[1/256^3] ^2 = 1/256^6 = 1/281,474,976,710,656. = 2.81* 10^14.
With four primes and 9 bits of the A/D converter, the odds are 1/ 512 ^4 = 1/ 68,719,476,736. This is 4,096 times more power of resolution. For four primes and 12 bits of the A/D converter, the resolution is 1/281,474,976,710,656 or 16,777,216 times more power of resolution compared to the three primes and 8 bit A/D converter. For two or more pixels the resolution increases exponentially to much greater odds (equation 2).
Note: The above detection Figures of a star by the simulator, is based on 3 primes and 8 bit per prime.
The Achievable four primes and 12 bit per prime provides brightness resolutions of magnitudes of 10^14 better than 10^10 desired by NASA. This technology provides 10^4 or 10,000 time better differentiations in brightness. To separate planets from stars, the combinations of visible light and IR should provide the masking (filter) that is required to detect an Earth like planet surrounded by water. The IR signature of H2O is quite distinct. For a four channel of IR and 12 bits per A/D converter, the equation for resolutions is:
- Probability of Detection = (1/d^p). (1/d^c). Equation 3
This provides resolutions of 1/281,474,976,710,656 * 281,474,976,710,656 = 1/ 2.8 * 10^28.
For two or more pixels the resolution increases exponentially to much greater odds given by.
- Probability of Detection = [1/d^p). (1/d^c)]^n. Equation 4
This technology is posed to resolve the masking and starlight suppression problems economically in shorter time.