There have been some algorithms that use image processing for power quality disturbances identification. This algorithms firstly converts 1-D power signal to 2-D image, then use image algorithms to classify power qual...
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ISBN:
(纸本)9781538604328
There have been some algorithms that use image processing for power quality disturbances identification. This algorithms firstly converts 1-D power signal to 2-D image, then use image algorithms to classify power quality disturbances. Different from these methods, a new method is presented here. Converts 2-D texture image algorithms to 1-D waveform algorithms, then use a classifier(here is SVM) for recognize power quality disturbances. These 1-D features include level Co-occurrence Matrix, Markov random field, voltage values and gradient firstly. The experiment show that the algorithm can reach a good result.
There have been some algorithms that use image processing for power quality disturbances *** algorithms firstly converts 1-D power signal to 2-D image,then use image algorithms to classify power quality *** from these...
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There have been some algorithms that use image processing for power quality disturbances *** algorithms firstly converts 1-D power signal to 2-D image,then use image algorithms to classify power quality *** from these methods,a new method is presented *** 2-D texture image algorithms to 1-D waveform algorithms,then use a classifier(here is SVM) for recognize power quality *** 1-D features include level Co-occurrence Matrix,Markov random field,voltage values and gradient *** experiment show that the algorithm can reach a good result.
Nowadays the automobile industry is becoming more and more demanding as far as quality is concerned. Within the wide variety of processes in which this quality must be ensured, those regarding the squeezing of the aut...
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Nowadays the automobile industry is becoming more and more demanding as far as quality is concerned. Within the wide variety of processes in which this quality must be ensured, those regarding the squeezing of the auto bodywork are especially important due to the fact that the quality of the resulting product is tested manually by experts, leading to inaccuracies of all types. In this paper, an algorithm is proposed for the automated evaluation of the imperfections in the sheets of the bodywork after the squeezing process. The algorithm processes the profile signals from a retroreflective image and characterizes an imperfection. It is based on a convergence criterion that follows the line of the maximum gradient of the imperfection and gives its geometrical characteristics as a result: maximum gradient, length, width, and area.
This paper reports the development of image processing methods for the detection of superficial changes related to quality deterioration in ready-to-use (RTU) leafy spinach during storage. The experiment was performed...
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This paper reports the development of image processing methods for the detection of superficial changes related to quality deterioration in ready-to-use (RTU) leafy spinach during storage. The experiment was performed on spinach leaves stored at 4.5 degrees C for 21 days (Set 1) and at 10 degrees C for 9 days (Set 2). Regarding Set 1, 75 units were evaluated beginning at time zero and after 7. 14, and 21 days of storage (treatments t(1.0), t(1.1), t(1.2), and t(1.3), respectively). In the case of Set 2.24 samples were measured at time zero and after 3, 6, and 9 days (treatments t(2.0), t(2.1), t(2.2), and t(2.3), respectively). Multispectral images were acquired using a 3-CCD camera centered at the infrared (IR), red (R), and blue (B) wavelengths. Opportune combinations of these bands were calculated using virtual images, and a non-supervised classification was performed. A large number of spinach leaves belonging to Set 2 showed injuries due to the effects of in-pack condensation;thus, an image algorithm was developed to separate these defective leaves before applying the classification. For Set 1, Set 2 and all the calculated virtual images, the classification procedure yielded two image-based deterioration reference classes (DRCs): Class A, including the majority of the samples belonging to t(1.0) and t(1.1) (Set 1) and to t(2.0) and t(2.1) (Set 2) treatments and Class B. which comprised mainly the samples belonging to t(1.2) and t(1.3) (Set 1) and to t(2.2) and t(2.3) (Set 2) treatments. An internal validation was performed, and the best classification was obtained with the virtual images based on R and B bands. For each sample, camera classification was evaluated according to reference measurements (visible (VIS) reflectance spectra and CIE L*a*b* coordinates);in all cases, VIS reflectance values corresponded well with storage days, and Classes A and B could be considered homogenous with regards to L* and a* values. Taken together, these results confirmed that
An ASIC implementation of capacitive fingerprint sensor is described for user authentication on small, thin, and portable equipment. New charge sharing sensing circuit minimizes the influence of internal parasitic cap...
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An ASIC implementation of capacitive fingerprint sensor is described for user authentication on small, thin, and portable equipment. New charge sharing sensing circuit minimizes the influence of internal parasitic capacitances and enlarges the voltage difference between a ridge and valley. A voltage comparator can easily discriminate a ridge and valley. Our method results in about 180% improvement in the voltage difference between a ridge and valley. The sensing circuit also includes a pixel-level automatic calibration scheme. The proposed calibration scheme initializes a capacitive fingerprint sensor LSI to eliminate the influence of the surface condition and environment, which is degraded by dirt during long-time use. The test chip is fabricated on a 0.35 mu m standard CMOS 1-poly 4-metal process. (c) 2006 Elsevier B.V. All rights reserved.
The application specific integrated circuit implementation of a capacitive fingerprint sensor system-on-chip (SOC), which embeds a 32-bit microcontroller for performing an identification algorithm, is described for us...
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The application specific integrated circuit implementation of a capacitive fingerprint sensor system-on-chip (SOC), which embeds a 32-bit microcontroller for performing an identification algorithm, is described for user authentication on small, thin, and portable equipment. The SOC is composed of 160 x 192 array cells with a sensor detection circuit and an embedded 32-bit reduced instruction set computer (RISC) microcontroller. The proposed sensor detection circuit increases the voltage difference between a ridge and valley about 80% more than conventional circuits and minimizes an electrostatic discharge influence by applying an effective isolation structure. The 32-bit RISC microcontroller is embedded by a latch base for low power and low complexity. The test chip was fabricated on a 0.35 mu m standard complementary metal oxide semiconductor 1-poly 4-metal process.
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