1. An efficient method of data mining to facilitate ready identification of desired features within imagery data dispersed among multiple spectral bands, comprising:
(a) selecting a wavelet type for use in said efficient method of data mining;
(b) providing means for manipulating said data, said means at least further capable of implementing the algorithm,
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\u2003where
GDFI2n(i, t) is a wavelet-based generalized difference feature index,
i refers to wavelength band i of a data collector,
t is a specified lag between wavelength bands,
h0, h1 . . . h2n\u22121 are high frequency coefficients
g0, g1 . . . g2n\u22121 are low frequency coefficients,
wherein, a number of said high and low frequency coefficients is determined upon establishing an order of a wavelet of said selected wavelet type,
n is a specified number of vanishing moments of said selected wavelet type, and
zi, zi+t. . . zi +(2n\u22121)t are data necessary to yield at least one said wavelet-based generalized difference feature index from a spectral signature of an image;
(c) establishing a set of wavelet-based generalized difference feature indices that may be generated later in said efficient method of data mining;
(d) initiating at least one said means for manipulating data by setting a maximum wavelet order limit, selecting wavelength bands and setting K=0 and setting T=1, where
K is a specified wavelet array index, and
T is an incremented specified lag, defined as a specified number of said wavelength bands skipped between ones of said selected wavelength bands;
(e) setting a lag limit defined as
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\u2003where m is a specified number of wavelength bands in a specified dataset;
(f) reading at least one said data set comprising said wavelength in said specified dataset bands into said means for manipulating;
(g) identifying and discarding said specified wavelength bands having compromised data;
(h) incrementing said K;
(i) incrementing said T by 1;
(j) computing a reduced set of difference-sum wavelength band ratios;
(k) generating at least one said established wavelet-based generalized difference feature index;
(l) generating a cube of each said established wavelet-based generalized difference feature index;
(m) selecting at least one of said established wavelet-based generalized difference feature indices;
(n) thresholding said selected pre-specified established wavelet-based generalized difference feature indices,
wherein said thresholding results in only said selected pre-specified established wavelet-based generalized difference feature indices being used henceforth;
(o) saving said thresholded selected pre-specified established wavelet-based generalized difference feature indices;
(p) determining if said lag limit has been met;
(q) if said lag limit has been met, determining if said maximum wavelet order limit has been met;
(r) if said lag limit has not been met, performing another iteration of steps (h) through (r) until said lag limit has been met;
(s) if said maximum wavelet order limit has been met, stopping; and
(t) if said maximum wavelet order limit has not been met, setting said T=1 and performing another iteration of steps (h) through (t) until said maximum wavelet order limit has been met,
wherein, if both said lag limit and said maximum wavelet order limit have been met, said efficient method of data mining is ended, resulting in an efficient identification of said desired features in said imagery data.
2. The method of claim 1 said imagery data comprising hyperspectral data.
3. The method of claim 2 said hyperspectral data comprising wavelengths in the spectra from about 300 to about 900 nanometers.
4. The method of claim 2 said hyperspectral data comprising wavelengths in the spectrum of visible light.
5. The method of claim 1 said means for manipulating said data comprising software running on at least one specially programmed computer.
6. The method of claim 1 wherein, in the step of setting said maximum wavelet order limit, said maximum wavelet order limit is set at sixteen (16).
7. The method of claim 1 selecting said wavelet type from the group consisting of Daubechies, Vaidyanathan, Coiflet, Beylkin, and Symmlet Wavelets.
8. The method of claim 7 selecting said Daubechies Wavelet as said wavelet type.
9. An efficient method of data mining to facilitate ready identification of desired features within a multi-band data set, comprising:
(a) selecting a wavelet type for use in said efficient method of data mining;
(b) providing means for manipulating said data, said means configured to perform the algorithm,
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\u2003where
GDFI2n(i, t) is a wavelet-based generalized difference feature index,
i refers to band i of a data collector,
t is a specified lag between bands,
h0, h1. . . h2n\u22121 are high frequency coefficients
g0, g1. . . g2n\u22121 are low frequency coefficients,
wherein, a number of said high and low frequency coefficients is determined upon establishing an order of a wavelet of said selected wavelet type,
n is a specified number of vanishing moments of said selected wavelet, and
zi, zi+t. . . zi+(2n\u22121)t are data necessary to yield at least one said wavelet-based generalized difference feature index from a spectral signature;
(c) establishing a set of wavelet-based generalized difference feature indices that may be generated later in said efficient method of data mining;
(d) initiating at least one said means for manipulating data by setting a maximum wavelet order limit, selecting bands and setting K=0 and setting T=1, where
K is a specified wavelet array index, and
T is an incremented a specified lag, defined as a specified number of said bands skipped between ones of said selected bands;
(e) setting a lag limit defined as
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\u2003where m is a specified number of bands in a specified dataset;
(f) reading at least one said data set comprising said bands in said specified dataset into said means for manipulating;
(g) identifying and discarding said specified bands having compromised data;
(h) incrementing said K;
(i) incrementing said T by 1;
(j) computing a reduced set of difference-sum band ratios;
(k) generating at least one said established wavelet-based generalized difference feature index;
(l) generating a cube of each said established wavelet-based generalized difference feature index;
(m) selecting at least one of said established wavelet-based generalized difference feature indices;
(n) thresholding said selected pre-specified established wavelet-based generalized difference feature indices,
wherein said thresholding results in only said selected pre-specified established wavelet-based generalized difference feature indices being used henceforth;
(o) saving said thresholded selected pre-specified established wavelet-based generalized difference feature indices;
(p) determining if said lag limit has been met;
(q) if said lag limit has been met, determining if said maximum wavelet order limit has been met;
(r) if said lag limit has not been met, performing another iteration of steps (h) through (r) until said lag limit has been met;
(s) if said maximum wavelet order limit has been met, stopping; and
(t) if said maximum wavelet order limit has not been met, setting said T=1 and performing another iteration of steps (h) through (t) until said maximum wavelet order limit has been met,
wherein, if both said lag limit and said maximum wavelet order limit have been met, said efficient method of data mining is ended, resulting in an efficient identification of said desired features in said multi-band data set.
10. An efficient method of data mining to facilitate ready categorization of a data set dispersed over multiple bands, comprising:
selecting at least one wavelet type for use in said method,
wherein said wavelet is selected to achieve optimum computational efficiency;
providing software for at least implementing an algorithm to calculate at least one wavelet-based generalized difference feature index (GDFI);
specifying a set of generalized difference feature indices to be calculated using said efficient method of data mining;
providing a software routine to select and process a reduced set of bands of said data set dispersed over multiple bands;
iterating a sub-routine of said routine while applying a lag limit and a maximum wavelet order limit to establish a number of iterations, said sub-routine to at least:
compute a reduced set of difference-sum band ratios;
calculate said generalized difference feature indices;
generate the cube of each said calculated generalized difference feature index;
select pre-specified ones of said calculated generalized difference feature indices;
threshold said selected calculated generalized difference feature indices,
wherein said thresholding results in only said selected calculated generalized difference feature indices being used henceforth; and
save said thresholded selected calculated generalized difference feature indices;
wherein, if both said lag limit and said maximum wavelet order limit have been met, said efficient method of data mining to facilitate ready categorization of a data set dispersed over multiple bands is ended, resulting in an efficient identification of said desired features in said data set.
11. A method that samples all band ratio combinations in hyperspectral data for use with rapid combinatorial computations that integrate wavelet and wavelet-variogram techniques for improved data anomaly filtering, detection and classification of imagery, comprising:
selecting at least one wavelet type for use in said method,
wherein said at least one wavelet type is selected to achieve optimum computational efficiency; and
providing software that displays results in a form that facilitates classification and feature extraction tasks while employing a least-ordered said wavelet that enables select features to be readily identified,
wherein executing said software yields band ratios that provide useful information in support of said classification and feature extraction tasks, and
wherein said method yields select said imagery with specific features highlighted by employing at least one generalized difference feature index (GDFI) band ratio and multiplying said generalized difference feature index (GDFI) band ratio by constants associated with coefficients of said at least one wavelet type, said GDFI band ratio defined by:
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where
GDFI2n(i, t) is a wavelet-based generalized difference feature index,
i refers to band i of a data collector,
t is a lag between bands,
h0, h1. . . h2n\u22121 are high frequency coefficients
g0, g1. . . g2n\u22121 are low frequency coefficients,
wherein, the number of said high and low frequency coefficients is determined upon establishing the order of said wavelet type,
n is the number of vanishing moments of said selected wavelet type, and
zi, zi+t . . . zi+(2n\u22121)t are data used to yield at least one said generalized difference feature index; and
wherein at least one said feature appears in a resultant display as a distinct color or shade lighter than the remainder of said imagery.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. An amplifier circuit apparatus comprising a first input and a second input for respectively applying a first input signal and a second input signal, the first input being arranged to control a first active device of a first common emitter circuit having a first output, characterised by first circuit means coupled to the first common emitter circuit and the second input so as to enable, when in use, the first common emitter circuit to generate an output signal at the first output, the output signal corresponding to an amplification of a difference between the first input signal and the second input signal by a differential gain.
2. An apparatus as claimed in claim 1, further comprising a second active device of a second common emitter circuit coupled to the second input and having a second output, the first circuit means being arranged to enable, when in use, the first and second common emitter circuits to generate the output signal between the first and second outputs, the output signal corresponding to an amplification of a difference between the first input signal and the second input signal by a differential gain.
3. An apparatus as claimed in claim 1 or claim 2, wherein the first circuit means comprises a third input to control a third active device and a fourth input to control a fourth active device, the first and second inputs being respectively coupled to the third and fourth inputs, and the third and fourth active devices being cross coupled.
4. An apparatus as claimed in claim 1, claim 2 or claim 3, further comprising at least one further circuit means arranged to mirror a predetermined amount of current flowing through the first andor second common emitter circuits so as to provide at least one predetermined function.
5. An apparatus as claimed in claim 4, wherein the predetermined function is the generation of a signal indicative of the output signal for controlling the output signal.
6. An apparatus as claimed in claim 5, wherein the at least one further circuit means comprises second circuit means comprising a fifth active device arranged to generate a first feedback component signal indicative of a first current flowing through the first active device.
7. An apparatus as claimed in claim 6 when dependent upon claim 2, wherein the second circuit means comprises a sixth active device arranged to generate a second feedback component signal indicative of a second current flowing through the second active device.
8. An apparatus as claimed in claim 6, wherein an amplitude of a third current flowing through the fifth active device is less than an amplitude of the first current.
9. An apparatus as claimed in claim 6 or claim 7, wherein an amplitude of a fourth current flowing through the sixth active device is less than an amplitude of the second current.
10. An apparatus as claimed in claim 4, wherein the at least one predetermined function is a prevention of the output signal comprising a current level that exceeds a predetermined current level.
11. An apparatus as claimed in claim 10, wherein the at least one further circuit means comprises third circuit means comprising a seventh active device arranged as a first integrated diode.
12. An apparatus as claimed in claim 11 when dependent upon claim 2, wherein the third circuit means comprises an eighth active device arranged as a second integrated diode.
13. An apparatus as claimed in claim 11 or claim 12, wherein an amplitude of a fifth current flowing through the seventh active device is lower than an amplitude of a first current flowing through the first active device.
14. An apparatus as claimed in claim 12, wherein an amplitude of a sixth current flowing through the seventh active device is lower than an amplitude of the second current.
15. A driver circuit for a laser device comprising the amplifier circuit apparatus as claimed in any one of the preceding claims.
16. An optical communications network comprising the amplifier circuit apparatus as claimed in any one of claims 1 to 14.