Patent Grant
7635853
This invention relates generally to the field of printers, and in particular to processing of digital data corresponding to optical reflections from the surface of a recording medium, in order to identify the type of recording medium.
In many types of printers, a printhead (for example, an inkjet printhead), including an array of marking elements, is controlled to make marks of particular sizes, colors, etc. in particular locations on recording media (sometimes generically called paper herein and used interchangeably with the term “media”) in order to print a desired image. In some types of printing systems (sometimes termed “page-width printers”) the array of marking elements extends across the width of the recording medium and the image can be printed one line at a time as the recording medium moves relative to the printhead. In other types of printing systems (sometimes termed “carriage printers”) the printhead or printheads are mounted on a carriage that is moved past the recording medium in a carriage scan direction as the marking elements are actuated to make a swath of dots. At the end of the swath, the carriage is stopped, printing is temporarily halted, and the recording medium is advanced. Then another swath is printed, so that the image is formed swath by swath.
In order to produce high quality images, it is helpful to provide information to the printer controller electronics regarding the type of the recording medium, such as whether it is a photo paper or plain paper, for example. For inkjet printing, knowing the type of recording medium before electronically preparing the image for printing is advantageous, because differences in ink-recording medium interactions on different recording medium can result in poor image quality, if the amount and timing of ink deposition is not controlled appropriately for the type of recording medium in the printer.
Using an optical sensor to detect the type of recording medium in a printer is known in the prior art. Some examples are disclosed in U.S. Pat. Nos. 5,764,251; 6,291,829; 6,325,505; 6,386,669; 6,561,643; 6,838,687; 6,914,684; 6,984,034; and co-pending U.S. patent application Ser. No. 12/037,970. Such an optical sensor assembly for recording medium type detection can optionally be attached to the printhead carriage of a carriage printer. In the same way that the printhead can mark on all regions of the paper by the back and forth motion of the carriage and by the advancing of the recording medium between passes of the carriage, a carriage-mounted optical sensor is able to provide optical measurements, typically of optical reflectance, for one or more regions of the paper. Other types of optical sensors include stationarily-mounted sensors that are positioned near the paper-advance path of the printer, so that one or more regions of the paper can be viewed by the sensor as the paper moves past it.
An optical sensor assembly for recording medium type detection typically includes one or more photosensors and one or more light sources, such as LED's, mounted such that the emitted light is reflected off the surface of the recording medium, and the reflected light is received in the one or more photosensors. LED's and photosensors can be oriented relative to each other such that the photosensor receives specular reflections of light emitted from an LED (i.e., light reflected from the recording medium at the same angle as the incident angle relative to the normal to the nominal plane of the recording medium) or diffuse reflections of light emitted from an LED (i.e., light reflected from the recording medium at a different angle than the angle of incidence).
Typically, the photosensor signals for specular and/or diffuse reflections of light from the surface of the recording medium are amplified and then processed to separate the signal from the background noise. The processed signal characteristics are then compared with known signal characteristics for different recording medium types, and the present recording medium type is identified.
One known way in which recording medium types can be distinguished from one another is the spatial frequency of the variation of optical reflectance from the surface of the recording medium. It is known, for example, that photo papers made for inkjet printing tend to have a specular optical reflectance that has a spatial frequency of variation that is dominated by high frequency components, and that plain papers tend to have a specular optical reflectance that has a spatial frequency of variation that is dominated by comparatively lower frequency components.
Automatic detection of recording medium type in a printer is desired to be: a) highly accurate, so that the correct recording medium type is dependably identified; b) fast, so that printing throughput is not adversely impacted due to waiting for identification of the recording medium type; c) robust, so that aging or contamination of the light source or photosensor, or different environmental conditions do not degrade the reliability of recording medium type identification; and d) simple so that it can be done with low cost.
Different trade-offs in recording medium type identification requirements can be made in different types of printing systems. For example, U.S. Pat. Nos. 6,325,505 and 6,561,643, disclose performing a Fourier transform on the reflectance data to quantify the spatial frequency components. This may be appropriate for printers that are always connected to a host computer, because Fourier transform analysis can require extensive processing in order to be done quickly. However, all-in-one printing systems that include a scanner as well as a printer; are sometimes operated in a standalone mode for copying photos and documents, and it is important that the recording medium type identification still be fast, accurate, reliable, and simple. In low-cost all-in-one printing systems the system controller may be a “system-on-a-chip” controller, which is inexpensive, but may not have the processing bandwidth for complex calculations for recording medium type identification.
Some types of simple and fast signal processing methods for photosensor reflectance data for recording medium type identification are found to be highly accurate when the printing system is new, but as the sensor assembly ages or the optical components become coated with ink mist or particulates, or encounter extreme environmental conditions, the resulting shift in the processed signal characteristics can cause occasional misidentification with a set of known signal characteristics so that accuracy of recording medium type identification is not as high as it was initially. As a result, occasional recalibration may be required in order to restore the accuracy. While such recalibration can be automatic and programmed into printer firmware for implementation, it is preferable in some embodiments to provide a signal processing method that is more robust in its accuracy of recording medium type identification.
Therefore, what is needed is a new method of signal processing that is simple, fast, robust, and effective in analyzing a frequency distribution for accurate identification of a type of recording medium or other body types by reflections from their surfaces.
A method is proved for analyzing frequency distribution of a reflection from a surface of a body to determine a type of body surface. Initially, a plurality of data points from a sensor that sense the reflection of the body's surface are provided. A first series of terms are summed together to provide a first magnitude, each term including a plurality of data points; wherein the plurality of data points being spaced apart by a first spacing. A second series of terms are summed together to provide a second magnitude, each term including a plurality of data points; wherein the plurality of data points being spaced apart by a second spacing. The first magnitude is compared to the second magnitude to determine the type of body surface.
Referring to
In the example shown in
In fluid communication with each nozzle array is a corresponding ink delivery pathway. Ink delivery pathway 122 is in fluid communication with nozzle array 120, and ink delivery pathway 132 is in fluid communication with nozzle array 130. Portions of fluid delivery pathways 122 and 132 are shown in
Not shown in
Also shown in
Also mounted on carriage 200 is an optical sensor (also called a carriage sensor) 210, as shown schematically in
Printhead chassis 250 is mounted in carriage 200, and ink supplies 262 and 264 are mounted in the printhead chassis 250. The mounting orientation of printhead chassis 250 is rotated relative to the view in
A variety of rollers are used to advance the recording medium through the printer as shown schematically in the side view of
The motor that powers the paper advance rollers is not shown in
Toward the rear 309 of the printer, in this example, is located the electronics board 390, which contains cable connectors 392 for communicating via cables (not shown) to the printhead carriage 200 and from there to the printhead. Also on the electronics board are typically mounted motor controllers for the carriage motor 380 and for the paper advance motor, a processor and/or other control electronics (shown schematically as controller 14 and image processing unit 15 in
Also shown in
Electrical leads 221, 222, and 223 from the photosensor 212 and the two LED's 216 and 218 are connected to a wiring board 220, and from the wiring board 220, to leads (not shown) that may be connected to an electronics board (not shown) that is attached to the carriage 200. It is preferable for an amplifier circuit to be physically close to the photosensor 212, because the photosensor output signal is relatively weak and it is important to avoid extraneous electrical noise, for example from printer motor cables, etc. The electronics board attached to carriage 200 may include the electronics for the powering of the LED's and for amplifying the photosensor signal.
Aperture 214 determines the range of angles of incident light rays that are able to pass to the photosensor 212, while the opaque region around the aperture blocks light rays outside this range of angles. The region of the recording medium that the photosensor “sees” depends not only on the geometry of the aperture, but also upon its orientation relative to the plane of the recording medium. This region that the photosensor “sees” will also herein be called the photosensor's field of view. In the example shown in
It is found that the signal received in photosensor 212 from specular reflections of light emitted from LED 218, is highly sensitive to the shape of the surface of the recording medium, and can be used as a means to detect generic paper types (such as glossy photo paper, matte photo paper, and plain paper) by the characteristics of the noise in the photosensor signal from an unmarked printing surface of recording medium. Unlike backside recording medium sensor 375, which can detect recording medium type as it is being fed from the paper tray, the carriage sensor assembly 210 cannot detect generic paper type until the recording medium has reached print zone 303. If the backside recording medium sensor 375 has not identified a specific paper type, the carriage sensor assembly 210 may be used. In particular, the specular LED 218 emits light (and not diffuse LED 216), while scanning across the recording medium prior to the printhead beginning its print job, and the signal is analyzed to determine the paper type, so that the proper data rendering can be done in image processing unit 15 for good image quality on that paper type. The choice of wavelength of the specular LED 218 is not critical, but can be blue, for example.
The photosensor signal is typically amplified electronically, and the amplified signal is sent to an analog to digital converter (ADC) to provide a set of digital data. Optionally, the DC component of the electronic signal is filtered out and the signal is biased prior to analog to digital conversion so that the resulting signal is roughly centered in the range of the ADC, thereby making efficient use of the range.
As is readily apparent from
During scanning of the recording medium, the photosensor sampling rate is set to 25 kHz, for example, as the carriage moves at 10 inches per second, thus providing specular reflection data from the recording medium at a resolution of 2500 data points per inch. The digitized data from the ADC is typically stored in memory for further processing. To improve the signal to noise ratio and reduce the data processing load, the digitized data can be summed or averaged over a plurality (such as five) of data samples from the ADC and related to each encoder reading. In order to further reduce high frequency noise in the data, a moving average of the data can subsequently be calculated.
A central aspect of the present invention is a method for analyzing the frequency distribution of the (optionally smoothed) digital data. Embodiments of this method include summing a first series of terms, where the terms include a plurality of data points that are spaced apart from one another by a first spacing, and comparing the magnitude of the sum of the first series to the sum of a second series of terms, where the terms of the second series include a plurality of data points that are spaced apart from one another by a second spacing. The comparison of the magnitudes of the sums is then correlated with known characteristics of magnitudes of such sums that are stored in printer memory in order to identify the type of recording medium.
An embodiment of the method will be described with respect to a simple example of sine waves of two different frequencies, in order to clarify how the analysis of the frequency distribution works.
Different groupings of data points are shown in
In one exemplary embodiment of the invention, a series of terms is summed in order to provide a magnitude FL, in which the spacing of the data points in the terms is L. In particular, in this embodiment:
FL=Sum[absolute value(Di+Di+2L−2Di+L)] (Equation 1)
In computing magnitude F1 for sine wave 410 (shown in
In practice, the summations of a series of data points could include on the order of a thousand or more terms. The magnitudes F1, F2, . . . F20 were calculated according to Equation 1 above, for data sets having the form of sine waves 410 and 420 (shown in
As can be seen from Table 1, the values of FL are periodic, such that for L=P, the magnitude FP=0; and for L=P/2, the magnitude FP/2 is the maximum value of FL. Furthermore, for both P=10 and P=20, the maximum value of FL is about 2500. This is due to calculating magnitudes for sine waves, as will be explained below.
First of all, referring to
Use of the trigonometric relationship:
sin(α+β)=(sin α cos β)+(cos α sin β)
provides a more general way to calculate some magnitudes at particular data point spacings. Expressing β in radians:
if β=2π (i.e., a full period); then sin(α+β)=sin α;
if β=π (i.e., half a period); then sin(α+β)=−sin α; and
if β=π/2 (i.e., one quarter a period); then sin(α+β)=cos α.
For L=P, the nth term of the sum FP has the form:
absolute value[sin(2πn/P)+sin(2πn/P)−2 sin(2πn/P)]=0;
so the entire sum FP=0 as stated above.
For L=P/2, the nth term of the sum FP/2 has the form:
absolute value[sin(2πn/P)+sin(2πn/P)−(−2 sin(2πn/P))]=
absolute value[4 sin(2πn/P)]
If there are N terms in the sum, then the magnitude of FP/2 is equal to 4N times the average of the absolute value of sin(2πn/P). If P is much larger than 1, then a good approximation of the average of the absolute value of sin(2πn/P) is given by integrating over the positive half cycle and dividing by half the period. In particular, integrating from 0 to π:
∫sin(2πx/P)dx=−(P/2π)(cos(π)−cos(0))=P/π
Dividing this result by half the period, i.e. by P/2, gives 2/π as the average of the absolute value of sin(2πn/P). Thus, if there are N=1000 terms in the sum FP/2, a good approximation (for P much greater than 1) is FP/2˜4N (2/π)=8000/π˜2546. As can be seen in Table 1 above, FP/2 for P=10 was calculated to be 2462, while FP/2 for P=20 was calculated to be 2526, which is within 1 percent of the approximate value of 4N (2/π). As can be seen, the magnitude FP/2 is independent of the period P, other than the fact that the approximation is better for larger P.
Similarly it can be shown that a good approximation for FP/4 (for P much greater than 1) is given by FP/4˜4N/π˜1273 if there are N=1000 terms. In Table 1 for P=20, FP/4 was calculated to be 1263, which is within 1 percent of the approximate value of 4N/π. For P=10 there is no integer value for P/4=2.5, so there is not a corresponding magnitude near 1273, although the average of the magnitudes F2 and F3 (i.e., 1231) is not far off.
In general, it is found for a data set having points given by: Dn=sin(2πn/P), the magnitude FL for data point spacing L, where FL is given by equation 1 above, is:
FL˜(4N/π)(1−cos(2πL/P)) (Equation 2)
In
Actual data sets corresponding to photosensor data for specular reflection from plain paper and photo paper, as shown by examples in
Calculation of magnitudes FL of a few sums of the form of Equation 1 can thus be used as a simple and fast way to distinguish between sets of photosensor data corresponding to plain paper (characterized predominantly by low frequency variation) and sets of photosensor data corresponding to photo paper (characterized predominantly by high frequency variation). The details of how the various magnitudes FL are compared depends on factors such as the spatial frequency of the data sampling of the photosensor relative to the spatial frequency inherent in the recording medium, as well as any preprocessing of the digitized data (such as summing, averaging, or computing a moving average) that is done prior to calculating FL. The comparison may be as simple as checking whether FL1>AFL2, where A is a constant and spacing L1 is not equal to spacing L2 (i.e., comparing two magnitudes of sums where the data points for the terms of one sum have a different spacing than the data points for the terms of the other sum). Other methods of comparison can be more complex in order to provide a more reliable distinction between types of recording medium.
In one embodiment ten different printers having nominally equivalent carriage sensor assemblies 210 were used to characterize twenty six different recording medium types, with two repeats of each recording medium type per printer. This provided 26×20=520 different data sets to analyze. Three magnitudes of FL(F1, F3 and F25) were computed for each of the 520 data sets. It was found that comparing the ratio R=F25/F1 (which tends to be large for low frequency data sets corresponding to typical plain papers) with a borderline V given by the linear equation of V=250(F3−38000)/60000=250 F3−158.3, if R>V the recording medium can be identified as plain paper, and if R<V the recording medium can be identified as photo paper. This method of distinguishing recording medium types was found to be about 99 percent accurate, as is shown by the graph of data points in
An alternative way to identify recording medium type is to observe the trends of the ratios R1=F5/F3, R2=F10/F5, and R3=F25/F10. For photo recording medium it is found in one embodiment that R1˜R2˜R3, as shown in
Furthermore, it has been found that the accuracy of identification of plain paper versus photo paper is improved by using more than one comparison of magnitudes FL (for example, the comparison described above with reference to
It has also been shown that an embodiment of the present invention is more robust against changes in external conditions or manufacturing variation than an embodiment of the method described in commonly assigned U.S. patent application Ser. No. 12/037,970. A way to characterize robustness is to change the intensity of the illumination from LED 218 and see how much change can be tolerated by the various methods for accurate recording medium identification. Intensity of illumination of an LED can be varied through pulse width modulation of the voltage applied to the LED, for example. It was found that an embodiment of the present invention could tolerate a change of ±7 counts of pulse width modulation, while an embodiment of the method described in U.S. patent application Ser. No. 12/037,970 could tolerate a change of ±3 counts of pulse width modulation. Therefore, the improved robustness of the method of the present invention is demonstrated.
In the embodiments above, Equation 1 has been used to determine the magnitudes of the sums of terms of data points having various spacings between data points. However, the method is not restricted to the use of Equation 1. In particular, it is contemplated that the absolute value of each term could be replaced by the square of each term, or other functions of each term in other embodiments. For comparing magnitudes of different sums where the data points have different spacings, the functions for the different sums could be the same functions, or they could be differing functions. In addition, it is not required that for sums involving three data points, the data points be equally spaced as in the previous embodiments.
More generally, embodiments of the present invention include computing a first summation S1=Sum[f1(a1Di−−b1Dj+c1Dk)] and a second summation S2=Sum[f2(a2Di−b2Dl+c2Dm)], and comparing the two sums in order to identify a type of recording medium. In the notation of these expressions: a1, b1, c1, a2, b2, and c2, are all numerical constants greater than zero; i, j, k, l, and m are integer values such that: k>j>i, m>l>i; m>k, and m>j; and f1 and f2 are functions. Functions f1 and f2 can be the same function as each other. In Equation 1, f1 and f2 are both the absolute value function. Also, in Equation 1, a1=a2=c1=c2=1, and b1=b2=2.
The invention has been described in detail with particular reference to certain preferred embodiments thereof, but it will be understood that variations and modifications can be effected within the spirit and scope of the invention. In particular, the scope of this present invention of a method for analyzing and comparing frequency distributions can be extended beyond identifying types of recording medium. Another use of such a method includes identifying a type or quality of sheet goods (such as plywood or lumber) being transported on a conveyor belt past a photosensor according to variations in the woodgrain or other surface characteristic of the sheet goods or body being transported. The method can also be extended to analysis of audio signals received by a microphone. For example, the method could be used to distinguish a baby's cry from a dog's bark in a baby monitoring device.
The invention has been described in detail with particular reference to certain preferred embodiments thereof, but it will be understood that variations and modifications can be effected within the spirit and scope of the invention.
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