Pub Date : 2017-09-01DOI: 10.1109/ISPA.2017.8073576
P. Burai, B. Harangi
Though the number of in vitro fertilization (IVF) has been rising continuously from the beginning of the new millennium, however the success rate of the implantations remained low. According to the statistics, the main reason of unsuccessful IVF relates to the woman factors. The aim of our research project is to provide an automatic image processing based decision support system for the gynecologists which tries to help medical experts to determine the most appropriate time for the insemination. In this paper, we present the first component of this tool, which deals with the preprocessing of the videos about the uterus for further examinations. It includes the segmentation of the video frames by fully convolutional neural network (FCNN) to determines the region of interest. The chosen model has been trained on 4000 images acquired during real hysteroscopic surgeries and tested on other 716 ones. We have achieved 92% segmentation accuracy regarding the correct recognition of the fundus.
{"title":"Pixelwise segmentation of uterine wall in endoscopic video frame using convolutional neural networks","authors":"P. Burai, B. Harangi","doi":"10.1109/ISPA.2017.8073576","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073576","url":null,"abstract":"Though the number of in vitro fertilization (IVF) has been rising continuously from the beginning of the new millennium, however the success rate of the implantations remained low. According to the statistics, the main reason of unsuccessful IVF relates to the woman factors. The aim of our research project is to provide an automatic image processing based decision support system for the gynecologists which tries to help medical experts to determine the most appropriate time for the insemination. In this paper, we present the first component of this tool, which deals with the preprocessing of the videos about the uterus for further examinations. It includes the segmentation of the video frames by fully convolutional neural network (FCNN) to determines the region of interest. The chosen model has been trained on 4000 images acquired during real hysteroscopic surgeries and tested on other 716 ones. We have achieved 92% segmentation accuracy regarding the correct recognition of the fundus.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127457159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/ISPA.2017.8073572
P. Burrascano, S. Laureti, M. Ricci, A. Terenzi, S. Cecchi
Modelling real-world audio devices is essential for controlling their nonlinear behaviour and making quality evaluations. In particular, the measurement of nonlinear distortion plays an important role in audio reproduction systems since these nonlinear distortions affect the listening experience. In this context, common interesting measures are the total harmonic distortion, the harmonic distortion of order n and the intermodulation distortion. Due to the complex nature of audio signals, intermodulation distortion is more interesting than total harmonic distortion and harmonic distortion of order n since it provides a prediction of the distortion related to harmonics combination in the human perception of sounds. In this paper, a procedure for intermodulation distortion prediction is presented based on the identification of the kernels of a generalized Hammerstein system excited by a suitable input signal. Tests have been carried out both in simulated and real-world scenarios thus confirming the validity of the proposed approach.
{"title":"A pulse compression procedure for an effective measurement of intermodulation distortion","authors":"P. Burrascano, S. Laureti, M. Ricci, A. Terenzi, S. Cecchi","doi":"10.1109/ISPA.2017.8073572","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073572","url":null,"abstract":"Modelling real-world audio devices is essential for controlling their nonlinear behaviour and making quality evaluations. In particular, the measurement of nonlinear distortion plays an important role in audio reproduction systems since these nonlinear distortions affect the listening experience. In this context, common interesting measures are the total harmonic distortion, the harmonic distortion of order n and the intermodulation distortion. Due to the complex nature of audio signals, intermodulation distortion is more interesting than total harmonic distortion and harmonic distortion of order n since it provides a prediction of the distortion related to harmonics combination in the human perception of sounds. In this paper, a procedure for intermodulation distortion prediction is presented based on the identification of the kernels of a generalized Hammerstein system excited by a suitable input signal. Tests have been carried out both in simulated and real-world scenarios thus confirming the validity of the proposed approach.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128808955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/ISPA.2017.8073581
Marko Šarlija, Fran Jurisic, Siniša Popović
In this paper we present a QRS detection algorithm based on pattern recognition as well as a new approach to ECG baseline wander removal and signal normalization. Each point of the zero-centred and normalized ECG signal is a QRS candidate, while a 1-D CNN classifier serves as a decision rule. Positive outputs from the CNN are clustered to form final QRS detections. The data is obtained from the 44 non-pacemaker recordings of the MIT-BIH arrhythmia database. Classifier was trained on 22 recordings and the remaining ones are used for performance evaluation. Our method achieves a sensitivity of 99.81% and 99.93% positive predictive value, which is comparable with most state-of-the-art solutions. This approach opens new possibilities for improvements in heartbeat classification as well as P and T wave detection problems.
{"title":"A convolutional neural network based approach to QRS detection","authors":"Marko Šarlija, Fran Jurisic, Siniša Popović","doi":"10.1109/ISPA.2017.8073581","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073581","url":null,"abstract":"In this paper we present a QRS detection algorithm based on pattern recognition as well as a new approach to ECG baseline wander removal and signal normalization. Each point of the zero-centred and normalized ECG signal is a QRS candidate, while a 1-D CNN classifier serves as a decision rule. Positive outputs from the CNN are clustered to form final QRS detections. The data is obtained from the 44 non-pacemaker recordings of the MIT-BIH arrhythmia database. Classifier was trained on 22 recordings and the remaining ones are used for performance evaluation. Our method achieves a sensitivity of 99.81% and 99.93% positive predictive value, which is comparable with most state-of-the-art solutions. This approach opens new possibilities for improvements in heartbeat classification as well as P and T wave detection problems.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125501454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/ISPA.2017.8073580
Yao Cheng, Marius Pedersen, G. Chen
Image quality assessment has become a meaningful research field due to the explosive growth of image processing technologies in imaging industries. It is becoming more usual to quantify the quality of an image using image quality metrics, rather than carrying out time-consuming psychometric experiments. However, there is little research on the performance of image quality metrics on quality enhanced images. In this paper, we focus on images that have been enhanced by sharpening. A psychometric experiment was designed with observers giving scores to different images enhanced by sharpening on a display in a controlled dark environment. The results showed that full reference image quality metrics performed well when sharpening did not improve the visual image quality, while in images where sharpening increased the visual quality the performance was lower. No reference image quality metrics show better predictions than full reference image quality metrics in most cases.
{"title":"Evaluation of image quality metrics for sharpness enhancement","authors":"Yao Cheng, Marius Pedersen, G. Chen","doi":"10.1109/ISPA.2017.8073580","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073580","url":null,"abstract":"Image quality assessment has become a meaningful research field due to the explosive growth of image processing technologies in imaging industries. It is becoming more usual to quantify the quality of an image using image quality metrics, rather than carrying out time-consuming psychometric experiments. However, there is little research on the performance of image quality metrics on quality enhanced images. In this paper, we focus on images that have been enhanced by sharpening. A psychometric experiment was designed with observers giving scores to different images enhanced by sharpening on a display in a controlled dark environment. The results showed that full reference image quality metrics performed well when sharpening did not improve the visual image quality, while in images where sharpening increased the visual quality the performance was lower. No reference image quality metrics show better predictions than full reference image quality metrics in most cases.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"520 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116265186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/ISPA.2017.8073571
Aljosa Dudarin, Goran Molnar, M. Vucic
A simple multiplierless decimation filter is cascaded-integrator-comb (CIC) filter. However, the CIC filter offering high folding-band attenuations introduces a high passband droop. The most popular technique for reducing the droop is connecting a low-order FIR filter called compensator in cascade with CIC filter. In this paper, we present a method for the design of simple multiplierless compensators based on the minimization of the peak-to-peak passband deviation. We form a simple design problem by representing each compensator coefficient as only one signed power of two. The optimum coefficients are found by using the exhaustive search. We show that the proposed compensators with three and five coefficients significantly reduce the droop in wide passbands despite their extremely simple structures, which contain only two and four adders, respectively.
{"title":"Simple multiplierless CIC compensators providing minimum passband deviation","authors":"Aljosa Dudarin, Goran Molnar, M. Vucic","doi":"10.1109/ISPA.2017.8073571","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073571","url":null,"abstract":"A simple multiplierless decimation filter is cascaded-integrator-comb (CIC) filter. However, the CIC filter offering high folding-band attenuations introduces a high passband droop. The most popular technique for reducing the droop is connecting a low-order FIR filter called compensator in cascade with CIC filter. In this paper, we present a method for the design of simple multiplierless compensators based on the minimization of the peak-to-peak passband deviation. We form a simple design problem by representing each compensator coefficient as only one signed power of two. The optimum coefficients are found by using the exhaustive search. We show that the proposed compensators with three and five coefficients significantly reduce the droop in wide passbands despite their extremely simple structures, which contain only two and four adders, respectively.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123909469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/ISPA.2017.8073566
A. Denisova, V. Sergeyev
This paper is devoted to EM clustering improvement using hierarchical multivariate histogram for probability density representation. We propose to store and operate with the image histogram by means of a special tree data structure. This allows to speed up computations in the case of multivariate input. We also answer the questions of the algorithm initialization and offer an initialization rule, which exploits the proposed histogram-tree structure. We have tested our algorithm modification and initialization rule using remote sensing images. Obtained results have confirmed that the modified algorithm is faster and the initialization rule provides better clustering in comparison with the traditional EM algorithm implementation.
{"title":"Using hierarchical histogram representation for the EM clustering algorithm enhancement","authors":"A. Denisova, V. Sergeyev","doi":"10.1109/ISPA.2017.8073566","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073566","url":null,"abstract":"This paper is devoted to EM clustering improvement using hierarchical multivariate histogram for probability density representation. We propose to store and operate with the image histogram by means of a special tree data structure. This allows to speed up computations in the case of multivariate input. We also answer the questions of the algorithm initialization and offer an initialization rule, which exploits the proposed histogram-tree structure. We have tested our algorithm modification and initialization rule using remote sensing images. Obtained results have confirmed that the modified algorithm is faster and the initialization rule provides better clustering in comparison with the traditional EM algorithm implementation.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125113174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/ISPA.2017.8073588
D. Sarkar, S. Palit
Human observers can easily assess the quality of a distorted image even without examining the original image as a reference. In contrast, proper formulation of the problem so that the task of quality assessment of an image can be performed automatically, is a complex task and hence, designing objective No-Reference (NR) quality measurement algorithms is indeed very difficult. Existing approaches identify the degradation from among a set of degradations commonly experienced during image transmission and handling. However, apart from the inadequacy of being unable to indicate the amount of degradation they tend to reflect the overall degradation rather than being sensitive to one kind of degradation. The problem of correctly assessing the level of degradation is crucial since the choice of an appropriate restoration technique is heavily dependent on this. Further, in practical situations, the problem is compounded by the presence of other degradations acting as confounding factors such as noise. The uniqueness of the proposed approach is that it is designed to work well in situations where JPEG compressed images have been subjected to noise. Its performance has been tested through simulations on a large number of images from several popular databases. Results have also been compared with those obtained from subjective tests on the same images.
{"title":"Blind determination of quality of JPEG compressed images","authors":"D. Sarkar, S. Palit","doi":"10.1109/ISPA.2017.8073588","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073588","url":null,"abstract":"Human observers can easily assess the quality of a distorted image even without examining the original image as a reference. In contrast, proper formulation of the problem so that the task of quality assessment of an image can be performed automatically, is a complex task and hence, designing objective No-Reference (NR) quality measurement algorithms is indeed very difficult. Existing approaches identify the degradation from among a set of degradations commonly experienced during image transmission and handling. However, apart from the inadequacy of being unable to indicate the amount of degradation they tend to reflect the overall degradation rather than being sensitive to one kind of degradation. The problem of correctly assessing the level of degradation is crucial since the choice of an appropriate restoration technique is heavily dependent on this. Further, in practical situations, the problem is compounded by the presence of other degradations acting as confounding factors such as noise. The uniqueness of the proposed approach is that it is designed to work well in situations where JPEG compressed images have been subjected to noise. Its performance has been tested through simulations on a large number of images from several popular databases. Results have also been compared with those obtained from subjective tests on the same images.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115521763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/ISPA.2017.8073567
A. Cheddad, H. Kusetogullari, Håkan Grahn
This paper proposes a preprocessing stage to augment the bank of features that one can retrieve from binary images to help increase the accuracy of pattern recognition algorithms. To this end, by applying successive dilations to a given shape, we can capture a new dimension of its vital characteristics which we term hereafter: the shape growth pattern (SGP). This work investigates the feasibility of such a notion and also builds upon our prior work on structure preserving dilation using Delaunay triangulation. Experiments on two public data sets are conducted, including comparisons to existing algorithms. We deployed two renowned machine learning methods into the classification process (i.e., convolutional neural network-CNN- and random forests-RF-) since they perform well in pattern recognition tasks. The results show a clear improvement of the proposed approach's classification accuracy (especially for data sets with limited training samples) as well as robustness against noise when compared to existing methods.
{"title":"Object recognition using shape growth pattern","authors":"A. Cheddad, H. Kusetogullari, Håkan Grahn","doi":"10.1109/ISPA.2017.8073567","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073567","url":null,"abstract":"This paper proposes a preprocessing stage to augment the bank of features that one can retrieve from binary images to help increase the accuracy of pattern recognition algorithms. To this end, by applying successive dilations to a given shape, we can capture a new dimension of its vital characteristics which we term hereafter: the shape growth pattern (SGP). This work investigates the feasibility of such a notion and also builds upon our prior work on structure preserving dilation using Delaunay triangulation. Experiments on two public data sets are conducted, including comparisons to existing algorithms. We deployed two renowned machine learning methods into the classification process (i.e., convolutional neural network-CNN- and random forests-RF-) since they perform well in pattern recognition tasks. The results show a clear improvement of the proposed approach's classification accuracy (especially for data sets with limited training samples) as well as robustness against noise when compared to existing methods.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"21 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133754552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/ISPA.2017.8073568
Panagiotis Tsinganos, A. Skodras
Falls can be severe enough to cause disabilities especially to frail populations. Thus, prompt health care provision is essential to prevent and restore any harm. The purpose of this study is to develop a smartphone-based fall detection system that can distinguish between falls and activities of daily living (ADL). The typical fall detection system consists of a sensing component and a notification module. Android devices, equipped with sensors and communication services, are the best candidates for the development of such systems. This work incorporates a threshold based algorithm, whose accuracy is enhanced by a k Nearest Neighbor (kNN) classifier. In addition, this paper proposes the implementation of a personalization and power regulation system. It achieves high fall detection accuracy, (97.53% sensitivity and 94.89% specificity), which is comparable to related works.
{"title":"A smartphone-based fall detection system for the elderly","authors":"Panagiotis Tsinganos, A. Skodras","doi":"10.1109/ISPA.2017.8073568","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073568","url":null,"abstract":"Falls can be severe enough to cause disabilities especially to frail populations. Thus, prompt health care provision is essential to prevent and restore any harm. The purpose of this study is to develop a smartphone-based fall detection system that can distinguish between falls and activities of daily living (ADL). The typical fall detection system consists of a sensing component and a notification module. Android devices, equipped with sensors and communication services, are the best candidates for the development of such systems. This work incorporates a threshold based algorithm, whose accuracy is enhanced by a k Nearest Neighbor (kNN) classifier. In addition, this paper proposes the implementation of a personalization and power regulation system. It achieves high fall detection accuracy, (97.53% sensitivity and 94.89% specificity), which is comparable to related works.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131455090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/ISPA.2017.8073564
Csilla Fulep, G. Erdei
It is known that human visual performance is influenced by the ocular pupil diameter. So that we can analyze its effect on visual acuity, we developed a high-accuracy measuring system by which the pupil size can be monitored simultaneously with visual acuity examinations. We used infrared illumination and a far-field imaging setup in order not to disturb the subject. In this paper we present our measuring system comprising a camera, an infrared reflector and a unique evaluation/controlling software. According to our experimental results continuous pupil monitoring is feasible with ±0.2 mm spatial accuracy. The new system allows us to reveal delicate tendencies in pupil accommodation during visual acuity tests.
{"title":"Far-field infrared system for the high-accuracy in-situ measurement of ocular pupil diameter","authors":"Csilla Fulep, G. Erdei","doi":"10.1109/ISPA.2017.8073564","DOIUrl":"https://doi.org/10.1109/ISPA.2017.8073564","url":null,"abstract":"It is known that human visual performance is influenced by the ocular pupil diameter. So that we can analyze its effect on visual acuity, we developed a high-accuracy measuring system by which the pupil size can be monitored simultaneously with visual acuity examinations. We used infrared illumination and a far-field imaging setup in order not to disturb the subject. In this paper we present our measuring system comprising a camera, an infrared reflector and a unique evaluation/controlling software. According to our experimental results continuous pupil monitoring is feasible with ±0.2 mm spatial accuracy. The new system allows us to reveal delicate tendencies in pupil accommodation during visual acuity tests.","PeriodicalId":117602,"journal":{"name":"Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127218251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：