Author: claimsbot

1. A beverage supply device which executes a beverage supply operation of discharging a beverage material and a diluting liquid into a cup to mix the beverage material and the diluting liquid,
the device comprising:
at least one rail;
a flavor card which displays the beverage, the flavor card being configured to retain inherent information such as a dilution ratio of a beverage;
control means for reading out the inherent information of the beverage retained by the flavor card to execute the beverage supply operation; and
a sensor which detects a size of the cup, said sensor being movable on said at least one rail,
wherein the control means executes the beverage supply operation based on the cup size detected by the sensor.
2. A beverage supply device which executes a beverage supply operation of discharging a beverage material and a diluting liquid into a cup to mix the beverage material and the diluting liquid,
the device comprising:
at least one rail;
information retaining means for retaining inherent information such as a dilution ratio of a beverage;
control means for reading out the inherent information of the beverage retained by the information retaining means to execute the beverage supply operation; and
a sensor which detects a size of the cup, said sensor being movable on said at least one rail,
wherein the control means executes the beverage supply operation based on the cup size detected by the sensor, further comprising:
a flavor card which displays the beverage, the flavor card being configured to retain information for use in reading out the inherent information of the beverage retained by the information retaining means.

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. A method of enhancing an appearance of a face within a digital image, comprising
using a processor;
generating in-camera, capturing or otherwise obtaining in-camera a collection of one or more reference images including a face;
identifying the face within the one or more reference images;
segmenting skin tone portions of the face including a forehead, one or two cheeks or a chin, or combinations thereof, from face features including one or two eyes or a mouth or combinations thereof; within the skin tone portions of the face;
identifying one or more blemish regions that vary in luminance at least a threshold amount from the skin tone portions;
acquiring a main image of a same or higher resolution than the one or more reference images, including capturing the main image using a lens and an image sensor, or receiving said main image following capture by a device that includes a lens and an image sensor, or a combination thereof;
applying one or more localized luminance smoothing kernels each to one of the skin tone portions identified within the face to produce one or more enhanced skin tone portions of the face, wherein the applying comprises applying the one or more localized luminance smoothing kernels to luminance data of the one or more skin tone portions identified within said face;
smoothing certain original color data of one or more regions of the main image that correspond to the same one or more blemish regions identified in the reference images to generate smoothed color data for those one or more blemish regions of the main image;
generating an enhanced version of the main image including an enhanced version of the face that has the luminance data of the one or more skin tone portions replaced with the enhanced skin tone portions of the face, and that also has the certain original color data of the one or more blemish regions replaced with the smoothed color data; and
displaying, transmitting, communicating or digitally storing or otherwise outputting the enhanced image or a further processed version, or combinations thereof.
2. The method of claim 1, further comprising tracking said face within a collection of said reference images.
3. The method of claim 1, wherein the localized luminance smoothing comprises blurring or averaging luminance data, or a combination thereof.
4. The method of claim 1, further comprising applying one or more localized color smoothing kernels to one or more additional sub-regions including one or more sub-regions of the one or more skin tone portions or non-skin tone face features, or a combination thereof, to produce one or more additional enhanced sub-regions, and wherein the one or more additional enhanced sub-regions of the corrected image further comprise pixels modified from original pixels of the face at least by localized color smoothing.
5. The method of claim 1, further comprising applying noise reduction or enhancement, or both, to one or more of the skin tone portions or non-skin tone face features or sub-regions thereof, to produce one or more further enhanced sub-regions and wherein the one or more further enhanced sub-regions of the corrected image further comprise pixels modified from original pixels of the face at least by localized noise reduction or enhancement, or both.
6. The method of claim 1, further comprising determining certain non-skin tone pixels within the one or more skin tone portions that do not comprise a threshold skin tone, and removing, replacing, reducing an intensity of, or modifying a color of said certain non-skin tone pixels, or combinations thereof.
7. The method of claim 1, wherein enhanced pixels of the one or more enhanced skin tone portions comprise enhanced intensities which comprise one or more functions of a relationship between original pixel intensities and local average intensities within the one or more original or enhanced skin tone portions, or combinations thereof.
8. The method of claim 1, further comprising detecting one or more mouth or eye regions, or combinations thereof, within the face, and identifying and enhancing a natural color of one or more sub-regions within the one or more mouth or eye regions, including one or more teeth, lips, tongues, eye whites, eye brows, iris’s, eye lashes, or pupils, or combinations thereof.
9. The method of claim 1, further comprising classifying the face according to its age based on comparing one or more default image attribute values with one or more determined values; and adjusting one or more camera acquisition or post-processing parameters, or combinations thereof, based on the classifying of the face according to its age.
10. A digital image acquisition device, comprising
a lens,
an image sensor and
a processor, and
a processor-readable memory having embodied therein processor-readable code for programming the processor to perform a method of enhancing an appearance of a face within a digital image, wherein the method comprises:
generating in-camera, capturing or otherwise obtaining in-camera a collection of one or more reference images including a face;
identifying the face within the one or more reference images;
segmenting skin tone portions of the face including a forehead, one or two cheeks or a chin, or combinations thereof, from face features including one or two eyes or a mouth or combinations thereof;
within the skin tone portions of the face, identifying one or more blemish regions that vary in luminance at least a threshold amount from the skin tone portions;
acquiring a main image of higher resolution than the one or more reference images, including capturing the main image using a lens and an image sensor, or receiving said main image following capture by a device that includes a lens and an image sensor, or a combination thereof;
applying one or more localized luminance smoothing kernels each to one of the skin tone portions identified within the face to produce one or more enhanced skin tone portions of the face, wherein the applying comprises applying the one or more localized luminance smoothing kernels only to luminance data of the one or more skin tone portions identified within said face;
smoothing certain original color data of one or more regions of the main image that correspond to the same one or more blemish regions identified in the reference images to generate smoothed color data for those one or more blemish regions of the main image;
generating an enhanced version of the main image including an enhanced version of the face that has the luminance data of the one or more skin tone portions replaced with the enhanced skin tone portions of the face, and that also has the certain original color data of the one or more blemish regions replaced with the smoothed color data; and
displaying, transmitting, communicating or digitally storing or otherwise outputting the enhanced image or a further processed version, or combinations thereof.
11. The device of claim 10, wherein the method further comprises tracking said face within a collection of said reference images.
12. The device of claim 10, wherein the localized luminance smoothing comprises blurring or averaging luminance data, or a combination thereof.
13. The device of claim 10, wherein the method further comprises applying one or more localized color smoothing kernels to one or more additional sub-regions including one or more sub-regions of the one or more skin tone portions or non-skin tone face features, or a combination thereof, to produce one or more additional enhanced sub-regions, and wherein the one or more additional enhanced sub-regions of the corrected image further comprise pixels modified from original pixels of the face at least by localized color smoothing.
14. The device of claim 10, wherein the method further comprises applying noise reduction or enhancement, or both, to one or more of the skin tone portions or non-skin tone face features or sub-regions thereof, to produce one or more further enhanced sub-regions and wherein the one or more further enhanced sub-regions of the corrected image further comprise pixels modified from original pixels of the face at least by localized noise reduction or enhancement, or both.
15. The device of claim 10, wherein the method further comprises determining certain non-skin tone pixels within the one or more skin tone portions that do not comprise a threshold skin tone, and removing, replacing, reducing an intensity of, or modifying a color of said certain non-skin tone pixels, or combinations thereof.
16. The device of claim 10, wherein enhanced pixels of the one or more enhanced skin tone portions comprise enhanced intensities which comprise one or more functions of a relationship between original pixel intensities and local average intensities within the one or more original or enhanced skin tone portions, or combinations thereof.
17. The device of claim 10, wherein the method further comprises detecting one or more mouth or eye regions, or combinations thereof, within the face, and identifying and enhancing a natural color of one or more sub-regions within the one or more mouth or eye regions, including one or more teeth, lips, tongues, eye whites, eye brows, iris’s, eye lashes, or pupils, or combinations thereof.
18. The device of claim 10, wherein the method further comprises classifying the face according to its age based on comparing one or more default image attribute values with one or more determined values; and adjusting one or more camera acquisition or post-processing parameters, or combinations thereof, based on the classifying of the face according to its age.
19. One or more non-transitory processor-readable media having embodied therein code for programming one or more processors to perform a method of enhancing an appearance of a face within an acquired main image by utilizing a collection of one or more reference images that include the face, wherein the method comprises:
identifying the face within the one or more reference images;
segmenting skin tone portions of the face including a forehead, one or two cheeks or a chin, or combinations thereof, from face features including one or two eyes or a mouth or combinations thereof;
within the skin tone portions of the face, identifying one or more blemish regions that vary in luminance at least a threshold amount from the skin tone portions;
applying one or more localized luminance smoothing kernels each to one of the skin tone portions identified within the face to produce one or more enhanced skin tone portions of the face, wherein the applying comprises applying the one or more localized luminance smoothing kernels to luminance data of the one or more skin tone portions identified within said face;
smoothing certain original color data of one or more regions of the acquired main image that correspond to the same one or more blemish regions identified in the reference images to generate smoothed color data for those one or more blemish regions of the acquired main image;
generating an enhanced version of the main image including an enhanced version of the face that has the luminance data of the one or more skin tone portions replaced with the enhanced skin tone portions of the face, and that also has the certain original color data of the one or more blemish regions replaced with the smoothed color data; and
displaying, transmitting, communicating or digitally storing or otherwise outputting the enhanced image or a further processed version, or combinations thereof.
20. The one or more processor-readable media of claim 19, wherein the method further comprises tracking said face within a collection of said reference images.
21. The one or more processor-readable media of claim 19, wherein the localized luminance smoothing comprises blurring or averaging luminance data, or a combination thereof.
22. The one or more processor-readable media of claim 19, wherein the method further comprises applying one or more localized color smoothing kernels to the one or more additional sub-regions including one or more sub-regions of the one or more skin tone portions or non-skin tone face features, or a combination thereof, to produce one or more additional enhanced sub-regions, and wherein the one or more additional enhanced sub-regions of the corrected image further comprise pixels modified from original pixels of the face at least by localized color smoothing.
23. The one or more processor-readable media of claim 19, wherein the method further comprises applying noise reduction or enhancement, or both, to one or more of the skin tone portions or non-skin tone face features or sub-regions thereof, to produce one or more further enhanced sub-regions and wherein the one or more further enhanced sub-regions of the corrected image further comprise pixels modified from original pixels of the face at least by localized noise reduction or enhancement, or both.
24. The one or more processor-readable media of claim 19, wherein the method further comprises determining certain non-skin tone pixels within the one or more skin tone portions that do not comprise a threshold skin tone, and removing, replacing, reducing an intensity of, or modifying a color of said certain non-skin tone pixels, or combinations thereof.
25. The one or more processor-readable media of claim 19, wherein enhanced pixels of the one or more enhanced skin tone portions comprise enhanced intensities which comprise one or more functions of a relationship between original pixel intensities and local average intensities within the one or more original or enhanced skin tone portions, or combinations thereof.
26. The one or more processor-readable media of claim 19, wherein the method further comprises detecting one or more mouth or eye regions, or combinations thereof, within the face, and identifying and enhancing a natural color of one or more sub-regions within the one or more mouth or eye regions, including one or more teeth, lips, tongues, eye whites, eye brows, iris’s, eye lashes, or pupils, or combinations thereof.
27. The one or more processor-readable media of claim 19, wherein the method further comprises classifying the face according to its age based on comparing one or more default image attribute values with one or more determined values; and adjusting one or more camera acquisition or post-processing parameters, or combinations thereof, based on the classifying of the face according to its age.

1. A method for detecting a prion protein in a sample, andor removing a prion protein from a sample, which method comprises contacting the sample with a compound of formula (I)
wherein
R3 is hydrogen or an aryl group substituent or R3 is a solid support optionally attached via a spacer;
Z represents an oxygen atom, a sulfur atom or NR4;
Y represents an oxygen atom, a sulfur atom or NR5;
wherein R4 and R5, which may be the same or different, represent hydrogen, optionally substituted alkyl containing 1 to 6 carbon atoms, optionally substituted phenyl, optionally substituted benzyl or optionally substituted \u03b2-phenylethyl; and
X1 and X2 both represent a nitrogen atom;
R1 represents a group \u2014(CH2)m-Q1, wherein m is from 0 to 7, and Q1 represents \u2014NR11R12, in which R11 and R12, together with the nitrogen atom to which they are attached, form an optionally substituted heterocycloalkyl group, and
R2 represents a group \u2014(CH2)n-Q2, wherein n is from 0 to 7, and Q2 represents \u2014CR21R22R23 or \u2014NR21R22, in which R23 represents hydrogen, alkyl, cycloalkyl or heterocyclo-alkyl, and R21 and R22, together with the carbon or nitrogen atom to which they are attached, form an optionally substituted cycloalkyl or optionally substituted heterocycloalkyl group;
wherein the compound has affinity for binding the prion protein.
2. A compound of formula (I)
wherein
R3 is hydrogen or an aryl group or R3 is a solid support optionally attached via a spacer;
Z represents NH;
Y represents NH;
X1 and X2 both represent a nitrogen atom;
R1 represents a group \u2014(CH2)m-Q1, wherein m is 2, and Q1 represents
piperidyl or piperazinyl, and
R2 represents a group \u2014(CH2)n-Q2, wherein n is 0 or 2, and Q2 represents 1-piperidyl or adamantyl.
3. A pharmaceutical composition comprising:
a compound according to claim 2, wherein R3 is hydrogen or an aryl group substituent; and
a pharmaceutically acceptable carrier.

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. A reconfigurable vector processor comprising:
a plurality of processor units, each comprising:
a control unit for decoding instructions and generating control signals;
a scalar unit for processing instructions on scalar data; and
a vector unit for processing instructions on vector data based on the generated control signals; and

a vector control selector for selectively providing control signals generated by one of the plurality of processor units to a vector unit associated with a different processor unit of the plurality of processor units.
2. The reconfigurable vector processor of claim 1, wherein the vector control selector comprises a vector control multiplexer associated with a first processor unit of the plurality of processor units for selectively coupling the vector unit of the first processor unit to the control unit of the first processor unit or to a control unit of a second processor unit of the plurality of processor units to selectively provide the one or more control signals generated by the first processor unit or the second processor unit to the vector unit of the first processor unit.
3. The reconfigurable vector processor of claim 1, wherein the vector control selector comprises a crossbar switch for receiving a plurality of respective control signals from one or more of the plurality of processor units and selectively providing one or more of the received plurality of respective control signals to the respective vector units of one or more processor units of the plurality of processor units.
4. The reconfigurable vector processor architecture of claim 1, further comprising a vector data connector for selectively coupling the vector unit of one processing unit to the vector unit of a processing unit providing the control signals.
5. The reconfigurable vector processor of claim 1, further comprising a plurality of vector control selectors, each vector control selector comprising a vector control multiplexer associated with a respective processor unit of the plurality of processor units.
6. The reconfigurable vector processor of claim 1, wherein the respective control units of one or more processor units, herein after referred to as master units, are coupled to the vector control multiplexer associated with a different processor unit.
7. The reconfigurable vector processor of claim 6, wherein one or more of the master units comprise a vector control multiplexer for selectively coupling the vector unit to the control unit of another master unit.
8. The reconfigurable vector processor of claim 1, wherein the scalar processor of each of the plurality of processor units can perform arithmetic, logical and shift operations.
9. The reconfigurable vector processor of claim 1 wherein each of the plurality of processor units further comprises an address generation unit component for generating the address of the next instruction to be executed by the processor unit.
10. The reconfigurable vector processor of claim 1, wherein the scalar processor of each of the plurality of the processor units can operate concurrently with their respective vector units.
11. The reconfigurable vector processor of claim 1, wherein the scalar processor of each of the processor units can operate autonomously from their respective vector units.
12. The reconfigurable vector processor of claim 1, wherein one or more of the plurality of processor units each further comprise one or more data multiplexers for selectively coupling the vector units of the one or more processor units together.
13. The reconfigurable vector processor of claim 1, wherein each vector unit comprises a plurality of computational units (CUs) each for processing data of a defined bit length.
14. The reconfigurable vector processor of claim 13, wherein each CU is configured to perform add and shift operations on received data.
15. The reconfigurable vector processor of claim 13, wherein each CU comprises:
a data register;
a plurality of bypass multiplexers coupled to the data register;
an arithmetic logic unit coupled to outputs of the plurality of bypass multiplexers;
a multiplication unit coupled to the outputs of the plurality of bypass multiplexers;
a loadstore unit coupled to the outputs of the plurality of bypass multiplexers and a memory; and
a moveshift unit coupled to the outputs of the plurality of bypass multiplexers and one or more other computation units.
16. A method of processing data using a reconfigurable vector processor comprising two or more processing units, each with a vector unit, the method comprising:
configuring the reconfigurable vector processor to provide a vector unit of a first size for processing vector data of the first size;
executing one or more instructions using the vector unit of the first size to process vector data of the first size;
reconfiguring the reconfigurable vector processor to change the size of the vector unit to a second size; and
executing one or more instructions using the vector unit of the second size to process vector data of the second size.
17. The method of claim 16, wherein configuring and reconfiguring the size of the vector unit comprises:
generating control signals for controlling the vector unit of a first processing unit; and
providing the generated control signals to the vector unit of the first processing unit and the vector unit of a second processing unit to provide a vector unit with a total size of the sum of the individual vector units of the first and second processing unit.
18. The method of claim 16, wherein configuring the size of the vector unit comprises:
providing appropriate control signals to one or more components of the reconfigurable vector processor comprising a vector control multiplexer, or data multiplexers.
19. The method of claim 16, further comprising:
executing instructions using one or more scalar processors of the reconfigurable vector processor when executing instructions using the vector unit.
20. The method of claim 16, further comprising:
configuring the reconfigurable vector processor to provide one or more additional vector units for processing vector data.