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Patent US20030142985 - Automated banding defect analysis and repair for document processing systems - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inPatentsUsing a system of computer modules operatively associated with a document processing machine, banding defect analysis is accomplished by analyzing specific test patterns via image processing. The banding defects are characterized in terms of quantitative parameters based on an analysis of the banding...http://www.google.com/patents/US20030142985?utm_source=gb-gplus-sharePatent US20030142985 - Automated banding defect analysis and repair for document processing systemsAdvanced Patent SearchTry the new Google Patents, with machine-classified Google Scholar results, and Japanese and South Korean patents.Publication numberUS20030142985 A1Publication typeApplicationApplication numberUS 10/060,651Publication dateJul 31, 2003Filing dateJan 30, 2002Priority dateJan 30, 2002Also published asUS6862414Publication number060651, 10060651, US 2003/0142985 A1, US 2003/142985 A1, US 20030142985 A1, US 20030142985A1, US 2003142985 A1, US 2003142985A1, US-A1-20030142985, US-A1-2003142985, US2003/0142985A1, US2003/142985A1, US20030142985 A1, US20030142985A1, US2003142985 A1, US2003142985A1InventorsMeera Sampath, Ronald Rockwell, D. Rasmussen, Ashok Godambe, Eric Jackson, Raj MinhasOriginal AssigneeXerox CorporationExport CitationBiBTeX, EndNote, RefManPatent Citations (12), Referenced by (54), Classifications (7), Legal Events (7) External Links: USPTO, USPTO Assignment, EspacenetAutomated banding defect analysis and repair for document processing systems
US 20030142985 A1Abstract
Using a system of computer modules operatively associated with a document processing machine, banding defect analysis is accomplished by analyzing specific test patterns via image processing. The banding defects are characterized in terms of quantitative parameters based on an analysis of the banding defect. Key features are extracted from the banding defect parameters. The key features are analyzed in a diagnostic engine, to determine the possible source of the defect. The identified source is correlated to a recommended repair service procedure. The diagnostic process may be augmented by also including machine data in the analysis. Images(16) Claims(49)
BACKGROUND OF THE INVENTION [0001] 1. Field of Invention [0002] This invention relates to malfunction diagnosis of banding defects in document processing systems based on defect feature analysis and machine data analysis. [0003] 2. Description of Related Art [0004] In document processing systems, it is well known that customer satisfaction can be improved and maintenance costs reduced if problems with copiers and printers can be fixed before they become serious enough to warrant a service call by the customer. Systems exist that enable printers and copiers to call for service automatically, when sensors detect certain operating parameters outside of permissible ranges. Generally, these systems take effect after a threshold has been reached where the degradation in image quality is directly observable by the user. Given the large number of operating parameters that need to be tracked during operation, a specific defect at a certain level may or may not be a significant problem. The overall affect of a specific defect depends on the cumulative values of the other parameters in the system. Systems do exist that attempt to diagnose failures in document processing systems based on image quality analysis of the print. Such a system for the diagnosis of copier performance over telephone lines is described in U.S. Pat. No. 5,365,310, incorporated herein by reference in its entirety. [0005] A system and method for automatically diagnosing image quality defects is described in U.S. patent application Ser. No. 104,759, filed on Dec. 16, 1999 and commonly owned with the subject invention. The disclosure of this application is incorporated herein in its entirety. In this system, image quality problems occurring in a document processing system are identified by applying image processing and pattern recognition techniques to analyze specific test patterns. The results are analyzed in conjunction with known generic or experienced unit specific machine data in a diagnostic expert system to determine the cause of the problem. Further systems are provided to determine and execute a recommended service procedure, such as a user guided repair, scheduled maintenance service, parts replacement, and the like. [0006] One of the most frequently encountered image defects is the banding defect which is symptomatic of a wide variety of malfunctions in the performance of a document processor. These defects manifest themselves as one or more lines, streaks, or bands extending across the document copy parallel to the short edge or the long edge, and may be periodic or non-periodic. They are caused by worn or damaged parts, foreign matter, electrical malfunctions, misalignment and other sources, within the print engine and the associated feed mechanisms and controls. [0007] It is a purpose of the methods and systems described in this application to provide a means by which banding defects may be identified, analyzed, and quantified. It is a further purpose to diagnose a probable source of the defect and propose to the user an appropriate repair or correction. SUMMARY OF THE INVENTION [0008] The systems and methods of this invention provide automated diagnosis and repair of malfunctions in document processing systems based on a banding defect analysis. [0009] The banding defect analysis is accomplished by using a series of computer modules and algorithms to analyze specific test patterns via techniques such as image processing and pattern recognition. As a result of analyzing the output of the document processor in response to specifically designed test patterns, the banding defects are characterized in terms of parameters that quantify the overall impairment of perceived image quality caused by the defect, as well as additional quantitative parameters that characterize the nature of the defect. The banding defect analysis is based on image analysis techniques including fourier analysis, filtering and human visual perception modeling. [0010] Key features are extracted from the banding defect analysis output to provide data for determining possible sources of the defect. A series of algorithms for extracting pre-determined features of the banding defects from the outputs of the banding defect analysis is stored. These algorithms utilize threshold values for such features and device specific specifications. Using these algorithms and the outputs of the banding defect analysis, the key features of a particular banding defect are established. [0011] In order to diagnose the source of the defect the key features are subject to analysis in a diagnostic engine, such as a bayesian network, a neural network, or a model based or rule based, diagnostic engine to determine the probable source of the defect. The diagnostic engine utilizes a matrix of malfunctions and defect features symptomatic of a particular malfunction. Once a source malfunction is identified within a predetermined certainty by the diagnostic engine, it is submitted to a repair planning module which determines the recommended service. The repair module refers to a stored reference file or table which relates the identified source to the repair service. [0012] The diagnostic system of this application, includes an image quality analysis module which identifies and characterizes a banding defect in terms of quantitative parameters and generates key features of the banding defect for further analysis. The output of the image quality analysis module is preprocessed for use in a diagnostic engine. The data is processed in diagnostic engine to correlate the key features of the banding defect to a malfunction which is the possible source of the defect. A recommended repair or service is selected by the repair planning module. The results are presented to the user through a user interface. A diagnostic controller controls and coordinates the operation of all of the modules. A memory is provided in operative association with the processing components of the diagnostic system to store the algorithms and data used in the analysis and diagnosis. BRIEF DESCRIPTION OF THE DRAWING [0013] The preferred embodiments of the invention will be described in detail, with reference to the following drawing in which: [0014] [0014]FIG. 1(a) is a functional block diagram illustrating an embodiment of a diagnostic system, according to the system of this application; [0015] [0015]FIG. 1(b) is a functional block diagram of a document processor system which is adapted for use with the diagnostic system of FIG. 1(a); [0016] [0016]FIG. 2 is a functional block diagram illustrating an alternate embodiment of a diagnostic system, according to the system of this application; [0017] [0017]FIG. 3 is a functional block diagram showing an embodiment of the image quality analysis module, according to the system of this application; [0018] [0018]FIG. 4 is a functional block diagram showing an embodiment of a machine data module, according to the system of this application; [0019] [0019]FIG. 5 is a functional block diagram showing an embodiment of a repair planning module, according to the system of this application; [0020] [0020]FIG. 6 is a workflow diagram showing an embodiment of the method of the system of this application using banding defect analysis; [0021] [0021]FIG. 7 is a workflow diagram showing an alternate embodiment of the method of the system of this application using banding defect analysis with machine data analysis; [0022] [0022]FIG. 8 is an example of a table of image quality feature values for banding defects; [0023] [0023]FIG. 9 is an example of a table of image quality feature values for banding defects based on conditional probabilities of the features given the malfunctions; [0024] [0024]FIG. 10 is an example of a table of machine data feature values for banding defects; [0025] [0025]FIG. 11 shows an example of a table of machine data feature values for banding defects based on conditional probabilities of the features given the malfunctions; [0026] [0026]FIG. 12 shows a sample test selection table for banding defects; [0027] [0027]FIG. 13 shows a sample failure Repair table; and [0028] [0028]FIG. 14 illustrates a sample algorithm for extraction of features from a given machine data set.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS [0029] As used in this application, document processing systems, include analog and digital copiers, printers, scanners, facsimiles, and multifunction machines. Said systems include those based on all direct and indirect marking technologies, both color and black and white, such as xerography, ink jet, liquid ink, lithography, and the like. [0030] [0030]FIG. 1(a) illustrates an embodiment of a general purpose diagnostic system 10, adapted, according to the system of this application, to the analysis of banding defects. Banding defects are the most common image defect experienced in document processing systems and are the most frequent cause of service calls. The analysis of the lines, streaks, and bands which comprise banding defects is no easy task, as such defects can reflect some 30 to 40 malfunctions which may occur in a document processing system. [0031] The diagnostic system 10 operates in association with a document processing system 100. The diagnostic system 10 can be part of a document processor, multifunction machine, printer, etc., or could be part of a general purpose computer server connected to the machine, or could be implemented as a stand alone appliance having appropriate plug in capability for operation with a variety of machines in many different environments. For illustration, the basic components of document processing system 100 are shown in FIG. 1(b) and consist of a print engine 110 which is served by a document feed 120 and a scanner 130. Scanner 130 could also be part of the diagnostic system 10, as shown in FIG. 1(a). In some instances, such as a stand alone appliance or where there is no scanner available in the document processing system, a separate diagnostic scanner could be used in addition to or instead of the scanner of the document processing system. A system controller 140 provides operating control of the system 100 in conjunction with memory 150. An array of sensors 160 is distributed throughout the system to monitor the performance of system 100 at key points. The sensors generate current system data which can be stored in memory 150 to provide historical and status data to assist in analysis of defects. Further system performance data can be obtained by monitoring operating signals and other characteristics of the document processing system 100. For simplicity such monitoring function is encompassed in the sensor array module for the purposes of this application. [0032] As indicated above, the document processing system 100 can include a wide variety of components and architectures. The embodiment of FIG. 1(a) is presented only as an example for illustrating the operation of the banding defect analysis system of this application. The system of this application is intended for use with document processing systems in general. [0033] The diagnostic system 10, as shown in FIG. 1(a), includes an image quality analysis module 1 which identifies and characterizes a banding defect in terms of quantitative parameters and generates key features of the banding defect for further analysis. Additionally, the user is prompted to input additional features describing the defect, such as for example, by the selection of one of a set of icons or images, or by answering a set of specific questions. The output of the image quality analysis module 1 and the user input data is adapted for use in a diagnostic engine 2 by a preprocessor 3. The data is processed in diagnostic engine 2 to correlate the key features of the banding defect to a malfunction which is a possible source of the defect. A probability of causation is be evaluated and a recommended repair or service is selected by the repair planning module 5. The results are presented to the user through user interface 6. User interface 6 may include a display screen and appropriate keypad (not shown). [0034] The diagnostic controller 4 controls and coordinates the operation of all of the modules 1 through 7 and 21-23. A memory 9 is provided in operative association with the processing components of the diagnostic system 10 to store the algorithms and data used in the analysis and diagnosis. Memory 9 may also be adapted to track the operation of the diagnostic system 10, by logging and categorizing data. In this manner a historic data base of error correction may be maintained for future reference by diagnostic engine 2. [0035] A test pattern selection module, as shown in FIG. 1(a), selects the appropriate test patterns to be copied or printed and subsequently scanned and the sequence in which the selected test patterns are to be copied or printed and scanned and analyzed. The sequence may be predetermined and stored as a look-up table, or, can be determined dynamically, during the diagnostic process based on optimizing criteria such as minimal number of sheets to scan, maximal discriminating power of the tests, or maximal accuracy of diagnosis. In addition, in specifying the test patterns and their sequence, the test pattern selection module may select the specific region of the scanned image to be analyzed, and the set of features that need to be extracted from the results of the banding defect analysis. [0036] The diagnostic system 10, as shown in FIG. 1(a), is adapted to consider all of the data generated by the image quality analysis module 1 and eventually, using historical and experimental data relating to the causes of banding defects and data relating to the service fixes for such causes, present instructions to the user to accomplish a recommended service agenda. [0037] At the end of a diagnostic cycle, after an automated repair system or the user have performed the appropriate repair action, the correction verification module 23 prompts the user or the system to run additional tests or print additional test prints to determine if the correction that was accomplished actually was successful in eliminating the banding defect. If not the user may be directed to perform additional repair actions or to contact service personnel. [0038] In the alternate embodiment shown in FIG. 2, machine data module 7 is added to diagnostic system 10. Machine data module 7 is connected to receive data with respect to both current and historical operational experience with the document processing system 100. Such data may be obtained from sensor array 160, directly from monitored operational data from print engine 110 and document feed 120, from stored historical data, or from concurrently performed machine test procedures. [0039] As part of the alternate embodiment, shown in FIG. 2, a test selection module 22 is used to select the appropriate sequence of banding defect tests and machine tests to be performed. For the banding defect analysis, the test selection module 22 selects the appropriate test patterns to be copied or printed and subsequently scanned and the sequence in which the selected test patterns are to be copied or printed and scanned and analyzed. In addition, the test selection module may select the specific region of the scanned image to be analyzed, and the set of features that need to be extracted from the banding metric. The test and sequence may be predetermined and stored as a look-up table, or, can be determined dynamically, during the diagnostic process based on optimizing criteria such as time needed to complete the test, maximal discriminating power of the tests, or maximal accuracy of diagnosis. [0040] The components of the document processing system 100 are operationally interconnected by means of a bus system 170 and may be connected to the diagnostic system 10 by means of a connecting network 8. The components are shown for clarity as independent functional modules, but it should be noted that the related functions can be implemented by a central microprocessor with algorithms controlling the functions. In addition it should be appreciated that any one of, or a portion of the components of the diagnostic system can be located anywhere, including on the actual document processing system itself, on a distributed network, or an adjacent or remote diagnostics location. The flexibility of location could be implemented through the use of wired or wireless links or any other known or later developed element(s) that is capable of supplying electronic data to and from the connected elements. Furthermore, network 8 can be any one of, or combination of, a bus system, a direct serial connection, direct parallel connection, a distributed network such as an intranet, a local area network, a metropolitan area network, a wide area network, a satellite communication network, an infrared communication network, the Internet, or the like. Furthermore the diagnostic system 10 and the document processing system 100 may not be connected by any electronic means at all in particular in the embodiment of FIG. 1a), rather the information is transferred solely through print samples or copy samples being transferred in some fashion (automatically or manually) from 100 to 10. [0041] As shown in FIG. 3, image quality analysis module 1 starts with a single test pattern or a series of test patterns 11 specifically designed to isolate a banding defect. Such patterns are well known and may consist of a uniform grey image or a series of uniform image segments at varying grey levels. The test patterns may further be hardcopy originals or original digital images. The test patterns are submitted to the document processing system 100 and copied one or more times in a variety of test sequences to provide data under varying circumstances, for example, with or without document feeder, enlarged or reduced, repetitive copies, and other steps which tend to isolate possible root causes of the banding defect. Alternately, or, in addition, the digital test patterns may be submitted as print jobs and printed using the print engine 110. The copy or print samples obtained from the scanner or the print engine, respectively, may be scanned, for diagnostic purposes, using either an external scanner or scanner 130 on the document processing system. The digital image data is analyzed to identify the presence of banding defects in banding defect analyzer 13. [0042] Banding defect analyzer 13 is a computer module capable of executing image analysis algorithms available in memory 9. Banding defect analyzer 13 processes the image data to characterize the banding defect in terms of quantitative parameters. The image quality analysis module 1 analyzes the image, using commonly known image processing techniques such as, for example, Fourier transform analysis, band-pass filtering, histogramming, edge detection, 1-D projections, segmentation, classification, artifact detection, FIR filtering, wavelet analysis, statistical analysis, pattern recognition techniques, or the like, to evaluate image quality parameters and/or identify defects in the image(s). A typical banding defect analysis of the image data will provide data such as the following: [0043] (i) One dimensional CIE Lab L* profile data in one or more directions [0044] (ii) FFT data FFT(L*) [0045] (iii) L* profiles filtered through a human visual perception model, for example with a simple band pass filter (Lf Visually filtered L*) [0046] (iv) Visual Bands & Streaks VBS number which provides a measure of the overall impairment of perceived image quality caused by the banding defect [0047] (v) Locations and amplitudes of peak positive and negative deviations from average across the image of the L* signal (LSMinValue, LS MinLocation, LSMaxValue, LSMaxLocation) [0048] (vi) Locations and amplitudes, of peak positive and negative deviations from average across the image, of the visually filtered Lf signal (VFMinValue, VFMinLocation, VFMaxValue, VFMaxLocation) [0049] It is to be noted that other signals related to visual perception such as reflectance values, or CIE XYZ values, or physical measurements such as reflectance values measured through scanner RGB filters may be used in place of the L* signal. [0050] After the banding defect analysis is performed on a scanned image, further processing in feature extraction processor 14, determines/generates the key features of the banding defect based on predetermined defect characteristics stored in processor 14. These characteristics or features will be selected from a group, such as the following: [0051] (i) Defect Presence (is a banding defect present or not?) [0052] (ii) Defect Orientation (is the defect i.e., a short edge defect, or, i.e., long edge defect?) [0053] (iii) Defect Spread (is the defect uniformly observed across the page, or is it isolated in one or more locations on the page?) [0054] (iv) Defect Polarity (is the defect dark or light?) [0055] (v) Defect Type: Defect Spread and Polarity [0056] (vi) Defect Separation (if multiple isolated defects are present, what is the separation between them?) [0057] (vii) Defect Spectral Properties (is the defect observed in specific (known) frequencies?; is the defect present at high frequencies?) [0058] (viii) Defect Change with Change of Gray level: (is the defect present in a dark image and not present in a light Image?/ is the defect light on a dark image and dark on a light image?) [0059] (ix) Defect Enlargement/Reduction with Image (does the defect magnify (reduce) if the original image is magnified (reduced)?) [0060] The extraction process is accomplished by executing a series of algorithms based on the banding defect analysis outputs. The basic steps of the analysis involve the comparison of the banding analysis outputs or quantities derived therefrom, to predetermined maximum or minimum thresholds and device specific specifications. Examples of device specific specifications include the distance between fuser fingers, diameter of the donor roll, and length of the photoreceptor pitch, in the document processing system whose malfunctions are to be detected. The extraction process algorithms are described in simple terms below to illustrate the analysis. [0061] (i) Defect Presence: Check if the VBS number is greater than a given threshold. This threshold can be obtained from the IQ specifications for the copier/printer to be diagnosed, or by analyzing sample prints obtained from a copier/printer with no IQ related failures and obtaining the average VBS number over a set of prints. This algorithm can be used to check for the presence of the defect in images obtained by using different modes on the document processing system such as making a copy using the platen glass, making a copy using the automated document feeder, printing an internal test pattern, or printing an image submitted through the digital front end. [0062] (ii) Defect Orientation: If the VBS number is greater than the threshold value for a segment of the scanned image that runs along the long edge of the sample print, then defect orientation=short edge; if the VBS number is greater than the threshold value for a segment of the scanned image that runs along the short edge of the sample print, then defect orientation=long edge. [0063] (iii) Defect Type: Find the peak values within the L* peaks, or the visually filtered peaks, or, both (LSMinValue and LSMaxValue, or VFMinValue and VFMaxValue, or both) that exceed given thresholds. These thresholds can be predetermined fixed numbers, or can be based on the current image and can be equal to a significant statistic such as mean+3 std deviation, where the mean and standard deviation are computed over all Min and Max peak values across the image. Peak values that exceed the threshold will be characterized as “isolated defects”. Further, depending on whether the peak is a negative(Minvalue) or a positive one (MaxValue), the Defect Type will be set to “IsolatedDark” or IsolatedLight”. All peak values that do not cross the threshold to be marked as isolated defects but which still exceed a lower threshold will be marked as “potential defects”. If the number of negative potential defects is greater than a specified threshold, then Defect Type=“Uniform Dark”; If the number of positive potential defects is greater than a specified threshold, then Defect Type=“Uniform Light”; [0064] (v) Defect Separation: Compute the distance between defects marked as isolated or potential by computing the difference between their locations [0065] (vi) Defect Spectral Properties: Check if the Fourier transform amplitudes at specific frequencies are significantly higher than average; check if the Fourier transform amplitudes are significantly lower at high frequencies. [0066] (vii) Defect Change with Change of Gray level: Check if defect is present (as explained in (i) above) in a light image and not in a dark image and vice versa; Check if the defect polarity (as explained in (iv)above) changes from light to dark or vice versa in images of differing gray levels. [0067] (viii) Defect Enlargement/Reduction with Image: Compare the size of the defect in two images, one obtained by copying an original image (on the machine to be diagnosed) at one magnification level and the second obtained by copying the original at a higher/lower level of magnification. If the defect size is greater (lesser) with enlargement (reduction), defect is said to enlarge (reduce) with image. [0068] The extracted features are converted to a format acceptable to the particular diagnostic engine used, for example event format, in diagnostic preprocessor 3 and submitted to the diagnostic engine 2 for additional analysis. Diagnostic engine 2 is a computer module capable of executing algorithms which apply reasoning techniques such as qualitative reasoning, probabilistic reasoning, or fuzzy reasoning, and could be rule based, object based, or model based, or case based. A bayesian network is used in the preferred embodiment, but it should be noted that the system could be adapted to other forms of diagnostic algorithms. The output of the diagnostic engine 2 is sent to the diagnostic controller 4 for correlation with a repair scheme from repair planning module 5 and presentation to the user via user interface 6. [0069] In order to provide the diagnostic engine 2 with data from which to correlate particular extracted features with a particular malfunction which may cause the particular defect, a matrix of malfunctions is compiled with the likely defects that would result. For example in a particular document processing system the following malfunctions may occur which might result in a banding defect: [0070] (i) Photo Receptor Scratch (F1) [0071] (ii) Poor PhotoReceptor Ground (F2) [0072] (iii) Contaminated Charge Corotron (F3) [0073] (iv) Damaged Cleaner Blade (F4) [0074] (v) Contaminated Exposure Slot (ROS) (F5) [0075] (vi) Damaged Donor Roll (F6) [0076] (vii) Damaged Fuser Finger (F7) [0077] (viii) Contaminated CVT Glass (F8) [0078] To isolate the resulting banding defect, a test pattern having a uniform gray image, at a certain area coverage is scanned using several variations of steps. Since some of the defects caused by one or more of the above malfunctions only appear in alternate copies or only when the document feeder is used, the user is directed to make two sequential copies of the selected test pattern and also to make copies with and without the document feeder. [0079] A damaged fuser finger, for example, will cause repetitive streaks separated by a specific distance which correlates to the distance between the fuser fingers. Therefore, in analyzing the defect separation feature, this malfunction can be readily identified or eliminated depending on the value of defect separation. If such a malfunction is one of the set of malfunctions that the diagnostic system is designed to detect, then separation between defects would be extracted as a key feature. [0080] A matrix of such defect data based on experience with a fleet of document processors or with a particular processor can be compiled for use by the diagnostic engine 2. An example of such a matrix is shown in the table of FIG. 8. When the diagnostic engine used is a probabilistic one, such as a bayesian engine, the table includes conditional probabilities of the defect features given the malfunctions, as well as a prior probabilities of the malfunctions. An example of a defect feature matrix with probabilities is shown in FIG. 9. [0081] The diagnostic engine 2 performs the task of reasoning with the multiple sources of information to isolate the cause of the malfunction indicated by the banding defect. This can be an iterative process, for example, test pattern selection module 21 may decide, based on an initial diagnosis, that additional test patterns are to be scanned. In addition certain specific image quality parameters may be emphasized for evaluation and specific defect features may be extracted in order to further isolate the malfunction. [0082] In the embodiment of FIG. 1(a) Test pattern selection module 21 may be based on a simple look-up table that maps malfunctions to specific test patterns or it may be based on an optimization scheme. When the test pattern selection module is implemented as a simple lookup table, at any time during the iterative diagnostic process, the malfunction that appears most probable at that point may be chosen and a test pattern associated with that malfunction will be chosen as the next test pattern to scan and analyze. Where there is more than one test pattern associated with a failure, the test patterns will be scanned and analyzed in the order in which they are listed in the table, with the constraint that any test pattern scanned and analyzed already will not be repeated. In a case where the test pattern selection module is based on an optimization scheme, the next test pattern chosen will be that particular test pattern among the given set of test patterns that maximizes an optimization criteria, for example: the test pattern that maximizes the entropy function, provides the best discriminating information to isolate the malfunction, minimizes the time taken to reach a diagnosis, or minimizes the overall cost of running the diagnostic system, or a combination of the above. [0083] Based on the test pattern chosen by the test pattern selection module, the document processing system 100 is directed to produce one or more print samples of an original test pattern stored either in its internal memory, or on a network drive in a distributed network environment. The print samples are then forwarded to the scanner 130 or the external scanner for digitizing. Alternately, the customer, the customer service engineer, or the like, can make copies of hard copy original test patterns and then scan them. [0084] Alternately, in the embodiment of FIG. 2 in which the banding defect data is augmented with machine data obtained from sensor array 160, to provide better diagnosis of malfunctions, as illustrated in FIGS. 2 and 7, the test selection module 22 may decide that additional data must be collected from the document processing system to refine the current list of probable malfunctions obtained by analysis of the image data. [0085] As in the case of the test pattern selection module 21, the test selection module 22 may be based on a simple look-up table that maps malfunctions to specific tests or it may be based on an optimization scheme. When the test selection module is implemented as a simple lookup table, at any time during the iterative diagnostic process, the malfunction that appears most probable at that point may be chosen and a test associated with that malfunction will be chosen as the next test to perform. Where there is more than one test associated with a failure, the test will be performed in the order in which they are listed in the table, with the constraint that any test performed already will not be repeated. FIG. 12 shows a sample test selection look-up table. In a case where the test selection module is based on an optimization scheme, the next test chosen will be that particular test among the given set of tests that maximizes an optimization criteria, for example: the test that maximizes the entropy function, provides the best discriminating information to isolate the malfunction, minimizes the time taken to reach a diagnosis, or minimizes the overall cost of running the diagnostic system, or a combination of the above. [0086] The machine data obtained from the document processor may include machine operational data such as set point, actuator, and sensor data collected during regular operation of the machine, machine usage data, historical data such as fault counters and performance data. Such data may be obtained directly from the print engine 110, document feed 120, and other components of the document processor 100, from the sensor array 160, or from the memory 150. In addition, a set of special diagnostic or performance tests may be run on the document processing machine 100 and the effects of the tests can be observed on sensor array 120. Examples of such tests are described in U.S. Pat. Nos. 5,864,730; 5,893,008; 5,903,796; 5,937,224; 5,960,228; 5,946,521; 5,995,775; 6,016,204; 6,081,348; and 6,198,885, commonly owned with this application, the disclosure of which being incorporated herein in its entirety. The diagnostic tests typically are stress tests on the various components that for example, vary the actuators between maximum and minimum values. Examples of such diagnostic tests include but are not limited to photoreceptor uniformity tests, cleaner stress test, charge system stress tests, exposure system tests, development system tests, banding test on the photoreceptor and reload test on the photoreceptor. This data can be supplied to the machine data module 7 and may be factored into the analysis performed by diagnostic engine 2. [0087] Machine data module 7 is added to the diagnostic system for this purpose and provides the mechanism to collect and deliver machine data to diagnostic preprocessor 3 for consideration in the analysis performed by diagnostic engine 2. The machine data processor 19 generates a set of high level features from the machine data based on analysis of the machine data. The generated features diagnostic are converted to a format acceptable to the particular diagnostic engine used, for example event format, in diagnostic preprocessor 3. Some sample machine data features are listed below. [0088] (i) Uniformity Test: This feature is based primarily on the photoreceptor uniformity test data collected from the machine and is used to identify the presence of non-uniformity's such as scratches on the photoreceptor. This feature takes on values “Pass” or “fail”. [0089] (ii) Charge Test: This feature is used to identify charge corotron problems that may result in banding defects. It is based typically on the output of the charge stress test data. This feature takes on values “Pass” or “fail”. [0090] (iii) Cleaner Test: This feature is used to identify cleaner blade problems that may result in banding defects. It is based typically on the output of the cleaner stress test data. This feature takes on values “Pass” or “fail”. [0091] (iv) ROS Test: This feature is used to identify Ros exposure contamination problems that may result in banding defects. It is based typically on the output of the ROS stress test data. This feature takes on values “Pass” or “fail”. [0092] (v) Ground Test: This feature is used to identify photoreceptor ground problems that may result in banding defects. It is based typically on the output of the banding test data. This feature takes on values “Pass” or “fail”. [0093] Examples of machine data features and their correlation to malfunctions are shown in FIG. 10. A sample machine data feature matrix with conditional probabilities is shown in FIG. 11. [0094] The algorithms for extraction of the machine data features from the raw machine data are based on statistical analysis techniques, including, discriminant analysis, classifiers and regression analysis, and data mining techniques, including, decision trees and the like. The parameters of these tests are predetermined by analysis of machine data corresponding to the various malfunctions as well as data from a normal machine with no malfunctions. These parameters are stored in the machine data processor. The machine data generated during any run of the proposed diagnostic system is analyzed using these predetermined parameters and the feature values are determined. [0095] [0095]FIG. 14 illustrates a sample algorithm for extraction of features from a given machine data set. In this example, the machine data used is the output of the cleaner stress test and the banding test, and the features extracted are “Cleaner test pass” and “Cleaner test fail”. A discrimnant analysis based quadratic classifier is used to generate the features from the cleaner stress test data and the banding test data. [0096] Processing in preprocessor 3 may also involve conversion of analog data, received from one or more sensors, into qualitative values. Alternatively, it may involve translating machine signals into discrete event sequences, as described in U.S. Application No. 60/154,016, commonly owned with this application, the disclosure of which being incorporated herein in its entirety. The event sequences are recognized by the diagnostic engine 2. [0097] The features generated by the machine data processor and formatted by the diagnostic preprocessor are passed on to the diagnostic engine 2 to be used in conjunction with the defect features for isolating the malfunction. [0098] Machine data module 7 may take on a variety of architectures depending on the means by which the data is collected, an example of such a module is shown in FIG. 4. In FIG. 4 a machine interface 18 is a computer element which is programmed to receive data, such as historical data, signals from sensor array 160 and raw signals indicative of various operational status and events from the print engine, document feed and other components of document processing system 100. In some instances it may be desirable, as a result of an initial analysis, to run particular diagnostic tests to further isolate a suspected malfunction. This may be accomplished by machine test module 20 through machine interface 18 and controller 140 or directly through specific system components and the results of the tests may be received through sensor array 160 or memory 150. While the machine test module is shown to be part of the diagnostic system 10 in this embodiment, it may alternately be part of the document processing system 100. The data that is received from the interface 18 is processed in machine data processor 19 and features of the machine data are extracted for transmittal to preprocessor 3. [0099] The output of the diagnostic engine 2 is received by the repair planning module 5 for selection of the appropriate service procedure which will correct the malfunction identified in analysis. An example of repair planning module 5 is shown for illustration in FIG. 5. Memory 15 contains a table of repair procedures correlated with specific malfunctions which can be presented for selection by repair planning processor 12. In certain instances, it may be advantageous to review the scheduled maintenance service procedures stored in memory 16 for the purpose of accelerating such service to fix the malfunction. In addition memory 17 provides access to a systems history to allow the replacement of parts where warranted. The repair procedures may be performed by the customer, system administrator, key operator, or the like, or by certified service personnel. FIG. 13 shows a sample repair action table for a set of malfunctions. In this manner a probability of causation of a malfunction based on the key features can be calculated with a significant degree of accuracy and used to identify a repair which is likely to correct the banding defect. [0100] It should be noted that the above description identifies the components of the diagnostic system as separate functional modules. These functions can be implemented by a wide variety of computer elements, for example, a special purpose computer, a programmed microprocessor or microcontroller, a peripheral integrated circuit element, such as an ASIC, a digital signal processor, a hard-wired electronic or logic circuit, such as a discreet element circuit, a programmable logic device such as a PLD, PLA, FPGA, PAL, or in general, any device capable of implementing the functions described herein. [0101] The basic operation of the system of this application involves performing banding defect analysis on a print or copy sample of a special test pattern. Based on the output of the banding defect analysis, the presence of a defect (or set of defects) is detected and further, the defects are characterized by a set of quantitative parameters. Further processing of the banding defect analysis outputs is performed to extract the key features of the banding defects. The extraction of said key features is based on a a series of algorithms which utilize threshold values of the features and device specific specifications. [0102] The key features are analyzed in a diagnostic engine. This results in the computation of the most likely malfunctions among various malfunctions for a given set of key feature values. The process of analysis can be repeated through iterations designed to isolate the most probable malfunction based on a refined set of key feature values. The iterative process can consider image data, machine data, or user input data. A predetermined value of certainty can be set for the purpose of comparison and screening of the probable malfunctions. The analysis is deemed successful upon the selection of a malfunction having a certainty above a preset threshold certainty level. From this data, a suitable service procedure is selected designed to correct the malfunction. If the analysis fails to achieve the threshold of certainty, then a list of most probable causes given the analysis up to that point is displayed with suitable service procedure recommendations. If no malfunction is perceived, then the diagnostic service is aborted and the user is notified. [0103] As shown in FIG. 6, the process is started by initiating the diagnostic service. This activation can be manually started by the user after observation of banding defects on a document, automatically, as the result of image monitoring, or periodically according to service procedures. To first determine the presence of a banding defect, the user or system initiates the copying or printing of a specially designed test pattern followed by scanning the copy or print sample. The resulting image data is analyzed through the above steps to determine if there is a defect. This would be shown, for example, by certain elements of the banding defect analysis output exceeding threshold values or machine specific specifications. If no defect is found the diagnostic service is complete and the user is notified to exit to normal operation or to contact service personnel depending on whether the diagnostic system was invoked after the user observed a banding defect or not. [0104] The analysis process can cycle through a series of iterative steps by having a variety of test patterns available in storage. In addition the test patterns can be run in different settings or using different components to isolate the malfunction. Instructions can be presented to the user through the user interface or the cycles can be accomplished automatically as instructed by the diagnostic controller 4. [0105] The diagnostic data can be supplemented by machine data as previously described. As shown in the steps of FIG. 7, the machine data can be collected and analyzed sequentially, prior to, or after, the collection and analysis of the banding defect image data. [0106] In this manner a system and method of servicing a document processing machine is provided without relying on a service technician or other external process. This will result in considerable savings of time and cost. The system and method described herein can readily be adapted for significant automatic control. [0107] While the invention has been described with reference to specific embodiments, the description of the specific embodiments is illustrative only and is not to be construed as limiting the scope of the invention. Various other modifications and changes may occur to those skilled in the art without departing from the spirit and scope of the invention as defined by the claims. 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controlling a printer or copier using a toner mark band and reflex sensor working according to the triangulation principleUS7343105Jun 28, 2005Mar 11, 2008Ricoh Company, Ltd.Image forming apparatus and control method of image forming apparatus with operation based upon down event occurrencesUS7382507 *Nov 17, 2004Jun 3, 2008Xerox CorporationImage quality defect detection from image quality databaseUS7400339Sep 30, 2004Jul 15, 2008Xerox CorporationMethod and system for automatically compensating for diagnosed banding defects prior to the performance of remedial serviceUS7493055 *Mar 17, 2006Feb 17, 2009Xerox CorporationFault isolation of visible defects with manual module shutdown optionsUS7773774 *Jun 28, 2004Aug 10, 2010Xerox CorporationDynamic test pattern composition for image-analysis based automatic machine diagnosticsUS7911652 *Sep 8, 2005Mar 22, 2011Xerox CorporationMethods and systems for determining banding compensation parameters in printing systemsUS7953249Oct 4, 2006May 31, 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2006Ricoh Company, Ltd.Image forming apparatus, a control method of image forming apparatus, a computer program and a computer readable storage mediumEP1612053A3 *Jun 30, 2005Mar 29, 2006Ricoh Company, Ltd.Image forming apparatus, a control method of image forming apparatus, a computer program and a computer readable storage mediumEP1628166A2 *May 25, 2005Feb 22, 2006Xerox CorporationSystems and methods for correcting banding defects using feedback and/or feedforward controlEP1628166A3 *May 25, 2005Mar 1, 2006Xerox CorporationSystems and methods for correcting banding defects using feedback and/or feedforward controlEP1653295A1Sep 27, 2005May 3, 2006Xerox CorporationMethod and system for automatically compensating for diagnosed banding defects prior to the performance of remedial serviceWO2005101312A2 *Apr 13, 2005Oct 27, 2005AicFractal skr-method for evaluating image qualityWO2005101312A3 *Apr 13, 2005Oct 19, 2006AicFractal skr-method for evaluating image quality* Cited by examinerClassifications U.S. Classification399/9International ClassificationG03G15/00Cooperative ClassificationG03G15/5062, G03G15/5079, G03G15/55European ClassificationG03G15/50M, G03G15/55Legal EventsDateCodeEventDescriptionJan 30, 2002ASAssignmentOwner name: XEROX CORPORATION, CONNECTICUTFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:SAMPATH, MEERA;ROCKWELL, RONALD M.;RASMUSSEN, D. RENE;AND OTHERS;REEL/FRAME:012578/0054;SIGNING DATES FROM 20020116 TO 20020121Jul 30, 2002ASAssignmentOwner name: BANK ONE, NA, AS ADMINISTRATIVE AGENT, ILLINOISFree format text: SECURITY AGREEMENT;ASSIGNOR:XEROX CORPORATION;REEL/FRAME:013111/0001Effective date: 20020621Owner name: BANK ONE, NA, AS ADMINISTRATIVE AGENT,ILLINOISFree format text: SECURITY AGREEMENT;ASSIGNOR:XEROX CORPORATION;REEL/FRAME:013111/0001Effective date: 20020621Oct 31, 2003ASAssignmentOwner name: JPMORGAN CHASE BANK, AS COLLATERAL AGENT, TEXASFree format text: SECURITY AGREEMENT;ASSIGNOR:XEROX CORPORATION;REEL/FRAME:015134/0476Effective date: 20030625Owner name: JPMORGAN CHASE BANK, AS COLLATERAL AGENT,TEXASFree format text: SECURITY AGREEMENT;ASSIGNOR:XEROX CORPORATION;REEL/FRAME:015134/0476Effective date: 20030625Jun 30, 2005ASAssignmentOwner name: JP MORGAN CHASE BANK, TEXASFree format text: SECURITY AGREEMENT;ASSIGNOR:XEROX CORPORATION;REEL/FRAME:016761/0158Effective date: 20030625Owner name: JP MORGAN CHASE BANK,TEXASFree format text: SECURITY AGREEMENT;ASSIGNOR:XEROX CORPORATION;REEL/FRAME:016761/0158Effective date: 20030625Jul 16, 2008FPAYFee paymentYear of fee payment: 4Aug 9, 2012FPAYFee paymentYear of fee payment: 8Aug 23, 2016FPAYFee paymentYear of fee payment: 12RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services

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Bosnia risks delay to IMF funds after missing reform deadline | Daily Mail Online
Bosnia risks delay to IMF funds after missing reform deadline
SARAJEVO, April 5 (Reuters) - Bosnia has missed a deadline set by the International Monetary Fund for it to disburse aid under a 553 million euro loan deal, after its lawmakers failed to pass a key set of reforms on Wednesday. The deal will now likely have to be re-negotiated.
Bickering in parliament prevented the passage of laws that are part of a wider reform package devised by the European Union to guide Bosnia, mired in ethnic and political disputes, towards faster integration with the bloc.
The IMF had set April 5 as the deadline for the country to raise excise taxes on fuel to help build a pan-European highway, which would in turn unlock support for infrastructure projects by external lenders, such as the European Bank for Reconstruction and Development.
However, lawmakers refused to discuss the legislation, which was introduced as an emergency procedure, meaning they could not file amendments to it. Some said the hike in excise taxes would hit farmers hard and demanded that the government provide proof of alternative financing to compensate farmers for any losses.
Bosnia is made up of the Bosniak-Croat Federation and the Serb Republic which are linked via a weak central government. The regions are the main beneficiaries of the IMF aid, which they need to cover their respective budget gaps and to finance maturing debt.
"After today's parliament's vote... the 'reform agenda' has been buried," said Serb Republic Finance Minister Zoran Tageltija. "I think that Bosnia does not have an arrangement with the IMF anymore."
IMF spokespeople were not immediately available to comment.
The lender had said that unless Bosnia met the terms of the first review of its loan arrangement by the end of March, they would have to be re-negotiated and the disbursement of cash would be delayed for months.
An EU official in Bosnia said on Tuesday the lender would lower its growth forecast of 3 percent for the country this year. "This in turn could cause delays in other related programmes, including assistance from the European Union. It will also slow down the country's EU accession," Lars-Gunnar Wigemark, the head of EU Delegation in Bosnia, said in a statement.
The Bosnian regions, which have met most of the IMF's terms, will now have to rely on a World Bank $80.5 million loan to fill their budget gaps. They also plan to issue debt to finance maturing debt. ($1 = 0.9383 euros) (Editing by Hugh Lawson)

type: global

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ALASKA AK NATIVE AMERICAN ESKIMO WOMAN BREAST FEEDING CHILD NURSING BABY PHOTO 2 | eBay Please enable JavaScript Our new search experience requires JavaScript to be enabled. Please enable JavaScript on your browser, then try again.
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Antique Ornate White Frame Mirror | Etsy
Antique Ornate White Frame Mirror
Materials: wood, mirror, safety backing, hanging hardware
Danish Modern Frosted Glass Kluk Kluk Decanter. Mid-Century Modern Liquor Decanter.
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This is a beautiful large antique, hand carved, ornate, solid wood frame painted glossy white.The detailed flourishes and roses are Unique. There are no chips or structural cracks to frame. The white glossy paint with the enamel look gives it an updated modern look that looks stunning in any room! A statement piece that draws attention anywhere you place it.
We create our mirrors out of old refreshed wood frames, handcrafted frames and one of a kinds by local woodworkers. Each one is unique and one of a kind. Beveled mirror is available as an option.
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weight: 35 lbs. before crating.
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We offer free pick up or delivery in So. California for a minimal fee based on mileage.
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modern chic mirrors

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text: Translations English (2) Danish (1) French (1) German (1) Italian (1) License OSI-Approved Open Source (2) GNU General Public License version 2.0 (2) Programming Language C (1) C++ (1) Status 3 - Alpha (1) OS BSD (2) Grouping and Descriptive Categories (2) 32-bit MS Windows (2) All BSD Platforms (2) All POSIX (2) All 32-bit MS Windows (1) Linux (2) Mac (2) Modern (2) FreeBSD (2) Linux (2) Solaris (1) Win2K (1) WinXP (1) Windows (2) Top Apps

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Product - Beginnings of Literary Culture in the Ohio Valley
Beginnings of Literary Culture in the Ohio Valley
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Let Him First Be a Man nad Other Essays
Product - John Hancock, PH. D. a Memoir, with Selections from His Writings
John Hancock, PH. D. a Memoir, with Selections from His Writings
Product - School History of the United States
Product - The Amateur Actor; A Collection of Plays for School and Home
The Amateur Actor; A Collection of Plays for School and Home
Product - The Amateur Actor : A Collection of Plays for School and Home
The Amateur Actor : A Collection of Plays for School and Home

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ANIME DVD Marmalade Boy(1-76End+Movie)English subtitle&All region FREE SHIPPING | eBay
Details about ANIME DVD Marmalade Boy(1-76End+Movie)English subtitle&All region FREE SHIPPING
ANIME DVD Marmalade Boy(1-76End+Movie)English subtitle&All region FREE SHIPPING
233396083446
Last updated on 10 Nov, 2019 01:42:47 GMT View all revisions
Applicable Regions: As shown in the picture Sub-Genre: Comedy, Drama, Romance
ANIME DVD Marmalade Boy(1-76End+Movie)English sub... ANIME DVD Marmalade Boy(1-76End+Movie)English sub...


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With Milliner out, Darrin Walls starts at cornerback - New York Jets Blog - ESPN
With Milliner out, Walls starts at cornerback642dRich CiminiPlay1:35AFC East Q&A: Do Jets have the best defense?8mRich CiminiPlay1:53What does Sheldon Richardson's suspension mean for Jets' Wilkerson, Williams?13hDan GrazianoJets' Richardson will miss first meeting vs. Dolphins17hJames WalkerPlay1:53Jets' Richardson suspended four games by NFL18hPlay2:37Smith to throw to Marshall, other Jets in Chicago22hPlay1:38AFC East Q&A: Will rivals exploit Patriots' cornerbacks?1dMike ReissPlay1:11NFL Nation Buzz: Cam Newton, Robert Griffin III, Carson Palmer and more2dESPN.com staffPlay2:02Favre, 45, thinks he could still play in the NFL3dRob DemovskyPlay3:25Ranking every NFL franchise over the next three seasons3dNFL InsidersPlay1:46AFC East Q&A: What to do about Ndamukong Suh?3dJames WalkerPlay2:17AFC East Q&A: Which Bill will defenses focus on?4dMike RodakCan Jets rookie QB Bryce Petty make a serious run at Geno Smith's job?6dRich CiminiPlay2:19AFC East Q&A: Is there hope for Geno Smith?7dRich CiminiWith Milliner out, Walls starts at cornerback9/29/20130 SharesEmailComment
Syracuse University graduateFollow on Twitter0 SharesEmailPrintCommentNASHVILLE -- As expected, rookie cornerback Dee Milliner and running back Chris Ivory -- both with injured hamstrings -- were among the New York Jets' inactive players Sunday against the Tennessee Titans.The Jets announced before the game that Darrin Walls will start at cornerback, opposite Antonio Cromartie. This doesn't come as a huge surprise, but it doesn't reflect well on Kyle Wilson, who started last week for the demoted Milliner. Wilson might have lost the job because of his ill-timed implosion in the fourth quarter, when he lost his poise and committed a penalty on four straight plays.Wilson is expected to play in the nickel package.Without Ivory, the Jets went into the game with only three running backs -- Bilal Powell, Alex Green and rookie fullback Tommy Bohanon, who is capable of playing tailback in a pinch. The team didn't immediately announce who will replace Milliner, who was used as a nickel back last week after being demoted in the previous game.The Jets' other inactives are quarterback Brady Quinn, guard Will Campbell, tackle Oday Aboushi, tackle Ben Ijalana and linebacker Ricky Sapp.For the Titans, receiver Kenny Britt, the former Rutgers standout, is inactive. He's suffering from a rib injury. He also has fallen out of favor with the coaching staff and, according to ESPN Insider Adam Schefter, is on the trading block.The Titans' other inactives are former Jets running back Shonn Greene, linebacker Patrick Bailey, center Brian Schwenke, tackle Byron Stingily, defensive tackle Sammie Hill and defensive end Lavar Edwards.

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Cab Company in Honolulu, Hawaii with Reviews & Ratings - YP.comFind People•Advertise With UsBrowseFind PeopleFeatured CollectionsRestaurantsAutomotiveHome ImprovementHealth & WellnessAttorneysPeople Searched For...Military Surplus StoreUsed Car DealersAfrican Hair BraidingHk Usa MoversAutomobile SalvageGeneral ContractorsCar StorageWigs Hair PiecesUsed FurnitureArchitecture FirmsWeldersSports Cards MemorabiliaAuto Repair & ServiceFurniture StoresRoofing ContractorsKirby VacuumRestaurant Equipment Supply Wholesale ManufacturersEngineering FirmsSewing Machines Service RepairPropane RefillDentistsMexican RestaurantsNail SalonsAre you alocal business owner?Advertise with YPor call 888-367-2194What do you want to find?Search by business name, or keywordnearWhere? 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Honolulu Taxi Cab750 amana sthonolulu, HI 96814(808) 699-9999TaxisWebsiteAdd to mybookRemove from mybookAdded to your travel collection!Error when adding to travel collectionThis business was removed from the travel collection27. Royal Hawaiian Transportation431 Cooke StHonolulu, HI 96813(808) 223-1773TaxisLimousine ServiceWebsiteAdd to mybookRemove from mybookAdded to your travel collection!Error when adding to travel collectionThis business was removed from the travel collection28. Akamai Cab Co.BBB Rating: A+1785 S King St Ste 9Honolulu, HI 96826(808) 377-1379TaxisAirport TransportationAdd to mybookRemove from mybookAdded to your travel collection!Error when adding to travel collectionThis business was removed from the travel collection29. Castle Taxi1029 Kamehame DrHonolulu, HI 96825(808) 247-3321TaxisAdd to mybookRemove from mybookAdded to your travel collection!Error when adding to travel collectionThis business was removed from the travel collection30. A Reliable Shuttle & Taxi(1)BBB Rating: A+PO Box 88556Honolulu, HI 96830(808) 924-9292TaxisAirport TransportationWebsiteReliable is unreliable, this company takes your credit card and when it is time to pick you up they make you wait for 2 hours thats how long we wait…Platinum LimousineQuick & Affordable Car ServiceService Available 24/7. Call Now!1 Aloha Tower Drive, Honolulu, HI 96813www.LimoServiceNearYou.comShowing1-30
of 90results123NextAdd a New CollectionOops! There was a problem saving the new custom collection.Please try again.Collection Name cannot be emptyUpload a Photo(optional)Remaining Characters: 100Remaining Characters: 500CancelSaveMap ViewFeatured Taxisin Honolulu, HawaiiThe Cab738 Kaheka St, Honolulu, HI 96814(808) 422-2222WebsiteYP AdDirectionsMore InfoCoast TaxiKailua, HI 96734(808) 261-3755WebsiteYP AdMore InfoCharley's Taxi(1)1451 S King St #300, Honolulu, HI 96814(808) 233-3333WebsiteDirectionsMore InfoTom's Auto LLC1918 Kalani St, Honolulu, HI 96819(808) 832-7866Good Prices. Good CarsWebsiteYP AdDirectionsMore InfoAkamai Cab CoHonolulu, HI 96826(808) 944-3400YP AdMore InfoDidn't find what you were looking for?magnifying glassPlease help others by helping us do better.Suggest a BusinessHelpful Reviews City Taxicustomerrequest ratedI just wanted to commend one of your dispacher for her good and accurate service on the weekends. Her name is Carolyn and she taken my calls for my pre-arranged appointments for many years. I use to call on weekdays, but there were too many incidents where I had call backs telling me that they can't get a driver. Since then, I am using a different cab company and I'm using City Taxi only on the weekends. I am hoping that they could use Carolyn on weekdays too. Carolyn has always came through with all my pre-arranged appiontments and never failed me. She has a good attitude when talking to her and she always remembers my name, time of pick-up and destination. She does a great job and she is very experience with dispatching. Mahalo Carolyn!!! way to go!City Taxink2277 ratedService was very efficient- ca...Service was very efficient- cab driver arrived right on time and didn't mind that I had to run to an atm to get money. He was friendly, knew short cuts to get me to my location faster. Taxi was clean and ride was pleasant. Will use this taxi service again. Charley's Taxibalance808 ratedCharley's has been my #1 choice for taxi service every time I needed a cab. I live out Waipahu and they get me there nice and safe and the cabby's are always super friendly.AboutAbout UsSite FeedbackContact UsAdvertise with UsCareers - We're HiringCorporate BlogEngineering BlogLegal

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Zhengxuan Hotel Suzhou Xina - Ofertes d'hotels amb els MILLORS DESCOMPTES EN LÍNIA Fer un comentari al lloc web
No.10 South Wenhua Road, Nou Districte de Suzhou, Suzhou, Xina, 215000
Sobre Zhengxuan Hotel
Please note due to local licensing guidelines, Zhengxuan Hotel is only able to accept Mainland Chinese citizens. Guest must present a valid PRC Identification Card upon check-in. The hotel apologises for any inconvenience caused. Mostrar més

value: This website is a Singaporean online travel agency and metasearch engine for hotels, vacation rentals, flights, and airport transfer. It is owned by Agoda Company Pte.


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text: as sequestration in the pentagon and veterans. watch him on sunday at 10:00 a.m. eastern on c-span. >> saturday, but tv isn't savanna, georgia, for live coverage of the savanna, georgia but best of all. joining us will be al gore. and heidi kraft on her book, lessons i learned in a combat hospital. jerry wells asks why. the book festival is part of presidents day weekend coverage. >> the committee will come to order. the purpose of this hearing is to examine the efficiency of the department of homeland security and how likely they are spending taxpayer dollars. let me begin by extending a warm welcome to members of the subcommittee. i'm looking forward to working with ranking member ron barber and we share a strong commitment to support that we need to protect the homeland. we attended the dedication ceremony of the brian terry port of call center. i look forward to a strong bipartisan cooperation in helping to make the department of homeland security as efficient and effective as possible. i would like to introduce our freshman majority members. today we have mr. keith rothfus, mr. richard hud Search Results 0 to 1 of about 2

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3 bedroom detached house for sale in Violet Close, Basingstoke, Hampshire, RG22
www.rightmove.co.uk/property/61075324
Violet Close, Basingstoke, Hampshire
Situated in the very popular location of Kempshott stands this three bedroom link detached family home which has undergone extensive works throughout. The property includes a fabulous refitted bathroom and large kitchen/diner with central island. The property also benefits from refitted windows, central heating system and water softener. In addition this home has a separate boot room and utility room as well as larger glass roofed conservatory to the rear, leading to landscaped rear garden with large sandstone patio. Internal viewing is highly recommended.
To view this media, please visit the on-line version of this page at www.rightmove.co.uk/property-for-sale/property-61075324.html
Disclaimer - Property reference BAS170314. The information displayed about this property comprises a property advertisement. Rightmove.co.uk makes no warranty as to the accuracy or completeness of the advertisement or any linked or associated information, and Rightmove has no control over the content. This property advertisement does not constitute property particulars. The information is provided and maintained by Romans, Basingstoke. Please contact the selling agent or developer directly to obtain any information which may be available under the terms of The Energy Performance of Buildings (Certificates and Inspections) (England and Wales) Regulations 2007 or the Home Report if in relation to a residential property in Scotland.
22 May 2017 25 Violet Close, RG22 £375,000
09 Dec 2016 17 Violet Close, RG22 £370,000

value: This website is a UK-based company that runs rightmove.co.uk, the UK's largest online real estate portal and property website. It is listed on the London Stock Exchange and is a constituent of the FTSE 100 Index.