QR code encodes many kinds of information because of its advantages: large storage capacity, high reliability, full arrange of utter-high-speed reading, small printing size and high-efficient representation of Chinese...
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ISBN:
(纸本)9780819497925
QR code encodes many kinds of information because of its advantages: large storage capacity, high reliability, full arrange of utter-high-speed reading, small printing size and high-efficient representation of Chinese characters, etc. In order to obtain the clearer binarization image from complex background, and improve the recognition rate of QR code, this paper researches on pre-processing methods of QR code (Quick Response Code), and shows algorithms and results of image pre-processing for QR code recognition. Improve the conventional method by changing the Souvola's adaptive text recognition method. Additionally, introduce the QR code Extraction which adapts to different image size, flexible image correction approach, and improve the efficiency and accuracy of QR code image processing.
Timely and accurate detection of oestrus in cows is an essential element of the good management of dairy farms. At present, the detection of cows in oestrus by acoustic means is impeded by the problems of filtering, i...
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Timely and accurate detection of oestrus in cows is an essential element of the good management of dairy farms. At present, the detection of cows in oestrus by acoustic means is impeded by the problems of filtering, incomplete feature selection, and poor recognition accuracy. To overcome these difficulties, this study proposes a sound detection method for cows in oestrus based on machine learning technology using an optimal feature combination and an optimal time window. Firstly, a dual-channel sound detection tag consisting of a unidirectional microphone and an omnidirectional microphone (OM) was developed. The Least Mean Squares adaptive algorithm based on wavelet thresholds was used to filter the signals from the OM, and the dual-channel endpoint detection algorithm was used to identify the lowing of individual cows. The Friedman analysis was then used to select the sound features with significant differences before and after oestrus in terms of time, frequency, and cepstrum, and these were used to determine the most acceptable feature combination. We then analysed the effects of Back Propagation Neural Network (BPNN), Cartesian Regression Tree, Support Vector Machine, and Random Forest classification on the accuracy, precision, sensitivity, specificity, and F1 score of oestrus discrimination. Different time windows were used, and the discrimination performance of these algorithms was evaluated using the Area under Receiver Operating Characteristic Curve to find the most satisfactory match between the time window and the recognition algorithm. The dual-channel acoustic tag's accuracy, precision, sensitivity, and specificity results were 91.25, 98.83, 91.75, and 83.68%, respectively. BPNN with the 70 ms time window and the feature combination (spectral roll-off + spectral flatness + Mel-Freque ncy Cepstrum Coefficients) was confirmed as the most suitable oestrus recognition method. The average accuracy, precision, sensitivity, specificity, and F1 score of this met
At present,the somatosensory target system game equipment in the market is still in its *** to the current market situation and future development trends,this paper designs a home somatosensory target *** design uses ...
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At present,the somatosensory target system game equipment in the market is still in its *** to the current market situation and future development trends,this paper designs a home somatosensory target *** design uses computer vision technology to realize somatosensory operation,discarding the throwing objects in traditional target system games,making it convenient,safe and improving its *** thesis first analyzes the requirements and performance index of the home somatosensory target,determines the overall design concept,and then designs the recognition algorithm,using computer vision technology to determine the thrower's throwing posture,and then determine it the accuracy of its ***,the system is programmed to realize its various functions,and to debug the various functions of the *** have proved that the system can determine the posture of the body after the throw is completed by the relative positional relationship between the hand and the head in the image,and use this as a basis to determine the accuracy of the throw,and then give the corresponding *** system functions well and achieve the expected results.
Aiming at the problem that most of the face recognition algorithms can not overcome the complex conditions such as illumination, expression and occlusion, a face recognition algorithm based on Radon transform is propo...
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ISBN:
(纸本)9781510819085
Aiming at the problem that most of the face recognition algorithms can not overcome the complex conditions such as illumination, expression and occlusion, a face recognition algorithm based on Radon transform is proposed. In this algorithm, the face image is pre processed by using the normalized algorithm and wavelet transform. Then the Radon transform is used to extract the invariant features.
This study proposes a Multi-Level Screening Identification algorithm for the automatic extraction and identification of impact craters based on CCD imageConsidering of the comparatively low robustness of traditional a...
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This study proposes a Multi-Level Screening Identification algorithm for the automatic extraction and identification of impact craters based on CCD imageConsidering of the comparatively low robustness of traditional algorithms, we design analytical hierarchy process in the new algorithmFirst of all, we analyze the whole image, and tag the candidate regionsNext, make the different preprocessing for the different areas respectivelyThen, identify the craters from the different areaFinally, the algorithm will check the result of identification automaticallyTo test the algorithm, we choose the image from the H010, SI and CrisiumsThe proposed algorithm provides a good robustness in most of lunar areas.
S3C2410A microprocessor was the base of the *** system implemented unmanned communications station's door image collection by camera,and obtained the binary image after image *** system recognized the status of do...
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S3C2410A microprocessor was the base of the *** system implemented unmanned communications station's door image collection by camera,and obtained the binary image after image *** system recognized the status of door according to the recognition algorithm,which built on the rotation angle of the *** results demonstrate the feasibility and accuracy of this system,which has high practical utility.
This study proposes an improved Multi-Level Screening Identification algorithm for the automatic extraction and identification of impact craters based on CCD image. Compared with previous algorithm of Multi-Level Scre...
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ISBN:
(纸本)9781510829039
This study proposes an improved Multi-Level Screening Identification algorithm for the automatic extraction and identification of impact craters based on CCD image. Compared with previous algorithm of Multi-Level Screening Identification, we improved the robustness of algorithm based on terrain classification. First of all, our new algorithm makes a classification of terrain to get the areas of lunar mare and lunar highland and save the classification result by using binary image. At the same time, in order to get the entire suspected craters, our algorithm does the rough extraction and save them as the candidate regions. Next, the algorithm makes the different preprocessing for the different areas respectively according to the binary image of terrain classification. After eliminating many kinds of interference, our algorithm identifies the craters from the different areas as more accurate results. Finally, the algorithm will check the result of identification by the law of light on craters. After these steps, the algorithm can output the counting, the size and coordinates of craters. According to data from the Science and Application Center for Moon and Deepspace Exploration system, we choose the images from H010, Sinus Iridum and Mare Crisium for the testing separately. This algorithm shows higher robustness than previous work and achieves the expected results.
Background: Since the beginning of the COVID-19 pandemic, over 480 million people have been infected and more than 6 million people have died from COVID-19 worldwide. In some patients with acute COVID-19, symptoms man...
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Background: Since the beginning of the COVID-19 pandemic, over 480 million people have been infected and more than 6 million people have died from COVID-19 worldwide. In some patients with acute COVID-19, symptoms manifest over a longer period, which is also called "long-COVID." Unmet medical needs related to long-COVID are high, since there are no treatments approved. Patients experiment with various medications and supplements hoping to alleviate their suffering. They often share their experiences on social ***: The aim of this study was to explore the feasibility of social media mining methods to extract important compounds from the perspective of patients. The goal is to provide an overview of different medication strategies and important agents mentioned in Reddit users' self-reports to support hypothesis generation for drug repurposing, by incorporating patients' ***: We used named-entity recognition to extract substances representing medications or supplements used to treat long-COVID from almost 70,000 posts on the "/r/covidlonghaulers" subreddit. We analyzed substances by frequency, co-occurrences, and network analysis to identify important substances and substance ***: The named-entity recognition algorithm achieved an F1 score of 0.67. A total of 28,447 substance entities and 5789 word co-occurrence pairs were extracted. "Histamine antagonists," "famotidine," "magnesium," "vitamins," and "steroids" were the most frequently mentioned substances. Network analysis revealed three clusters of substances, indicating certain medication ***: This feasibility study indicates that network analysis can be used to characterize the medication strategies discussed in social media. Comparison with existing literature shows that this approach identifies substances that are promising candidates for drug repurposing, such as antihistamines, steroids, or antidepressants. In the context of a pandemic, the proposed met
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