Pub Date : 2022-01-13DOI: 10.4108/eai.13-1-2022.172816
M. Ansari, A. Agrawal, R. Khan
Adversaries always eager to take advantage of flaws in emerging healthcare digital solutions. Very few authors discussed durable application security. Therefore there is a need for a durable security mechanism that must be adequately efficient, is reliable, and defend critical data in an emergency situation. It ensures that the application can be serviced and meet the needs of users over an extended period of time. This paper presents the fuzzy TOPSIS based method to evaluate the behavioural impact for durable security in the context of the Digital India initiative. This paper also presents novel DURASec blueprints for trustworthy and quality healthcare application development.. Even though the advantages of such technologies may outweigh the dangers, hospitals, drugstores, clinics, practitioners, the drug industry as well as medical device manufacturers, should be prepared to identify and minimize security threats in order to protect sensitive healthcare data.
{"title":"DURASec: Durable Security Blueprints for Web-Applications Empowering Digital India Initiative","authors":"M. Ansari, A. Agrawal, R. Khan","doi":"10.4108/eai.13-1-2022.172816","DOIUrl":"https://doi.org/10.4108/eai.13-1-2022.172816","url":null,"abstract":"Adversaries always eager to take advantage of flaws in emerging healthcare digital solutions. Very few authors discussed durable application security. Therefore there is a need for a durable security mechanism that must be adequately efficient, is reliable, and defend critical data in an emergency situation. It ensures that the application can be serviced and meet the needs of users over an extended period of time. This paper presents the fuzzy TOPSIS based method to evaluate the behavioural impact for durable security in the context of the Digital India initiative. This paper also presents novel DURASec blueprints for trustworthy and quality healthcare application development.. Even though the advantages of such technologies may outweigh the dangers, hospitals, drugstores, clinics, practitioners, the drug industry as well as medical device manufacturers, should be prepared to identify and minimize security threats in order to protect sensitive healthcare data.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"21 1","pages":"7"},"PeriodicalIF":1.3,"publicationDate":"2022-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81752744","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 : 2022-01-13DOI: 10.4108/eai.13-1-2022.172817
Ying Zhang
Image denoising is very important in image preprocessing. In order to introduce the priori information of external clean image into the denoising process, a non-local clustering image denoising algorithm is proposed. A sparse representation dictionary is obtained by combining the image blocks of external clean image and internal noise image. The sparse coefficient estimation of ideal image is obtained by global similar block matching. Based on the class dictionary and the estimated sparse coefficient, a sparse reconstruction method based on compressed sensing technology is used to denoise the image. Experimental results show that compared with traditional image denoising methods, the proposed algorithm can significantly reduce the denoising block effect and preserve more details while transitioning more naturally in the flat area of the image.
{"title":"Non-local clustering via sparse prior for sports image denoising","authors":"Ying Zhang","doi":"10.4108/eai.13-1-2022.172817","DOIUrl":"https://doi.org/10.4108/eai.13-1-2022.172817","url":null,"abstract":"Image denoising is very important in image preprocessing. In order to introduce the priori information of external clean image into the denoising process, a non-local clustering image denoising algorithm is proposed. A sparse representation dictionary is obtained by combining the image blocks of external clean image and internal noise image. The sparse coefficient estimation of ideal image is obtained by global similar block matching. Based on the class dictionary and the estimated sparse coefficient, a sparse reconstruction method based on compressed sensing technology is used to denoise the image. Experimental results show that compared with traditional image denoising methods, the proposed algorithm can significantly reduce the denoising block effect and preserve more details while transitioning more naturally in the flat area of the image.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"25 1","pages":"14"},"PeriodicalIF":1.3,"publicationDate":"2022-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79166411","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 : 2022-01-05DOI: 10.4108/eai.5-1-2022.172783
Shuang Gao, Xiaowei Wang
In complex scenes, the accuracy of dance movement recognition is not high. Therefore, this paper proposes a deep learning and deformable part model (DPM) for dance movement feature extraction. Firstly, the number of filters in DPM is increased, and the branch and bound algorithm is combined to improve the accuracy. Secondly, deep neural network model is used to sample points of interest according to human dance movements. The features extracted from the DPM and deep neural network are fused. It achieves a large reduction in the number of model parameters and avoids the network being too deep. Finally, dance movement recognition is performed on the input data through the full connection layer. Experimental results show that the proposed method in this paper can get the recognition result more quickly and accurately on the dance movement data set.
{"title":"Feature extraction of dance movement based on deep learning and deformable part model","authors":"Shuang Gao, Xiaowei Wang","doi":"10.4108/eai.5-1-2022.172783","DOIUrl":"https://doi.org/10.4108/eai.5-1-2022.172783","url":null,"abstract":"In complex scenes, the accuracy of dance movement recognition is not high. Therefore, this paper proposes a deep learning and deformable part model (DPM) for dance movement feature extraction. Firstly, the number of filters in DPM is increased, and the branch and bound algorithm is combined to improve the accuracy. Secondly, deep neural network model is used to sample points of interest according to human dance movements. The features extracted from the DPM and deep neural network are fused. It achieves a large reduction in the number of model parameters and avoids the network being too deep. Finally, dance movement recognition is performed on the input data through the full connection layer. Experimental results show that the proposed method in this paper can get the recognition result more quickly and accurately on the dance movement data set.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"1 1","pages":"11"},"PeriodicalIF":1.3,"publicationDate":"2022-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75817372","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 : 2022-01-05DOI: 10.4108/eai.5-1-2022.172784
Jian Gao
Basketball posture recognition is one of the important research topics in human-computer interaction and physical education, which is of great significance in medical treatment, sports, security and other aspects. With the development of machine learning, the application value of basketball pose recognition in physical education is becoming more and more extensive. This paper constructs a novel convolutional neural network model to recognize basketball posture. The model consists of 11 layers. Convolution and pooling operations are carried out for five basketball postures in the sampled data set. By fusing with the features extracted from HOG, finer features can be obtained. Finally, the data set is trained and recognized by entering the full connection layer for classification. The results show that compared with the traditional machine learning methods, the recognition performance of new model is better.
{"title":"Basketball posture recognition based on HOG feature extraction and convolutional neural network","authors":"Jian Gao","doi":"10.4108/eai.5-1-2022.172784","DOIUrl":"https://doi.org/10.4108/eai.5-1-2022.172784","url":null,"abstract":"Basketball posture recognition is one of the important research topics in human-computer interaction and physical education, which is of great significance in medical treatment, sports, security and other aspects. With the development of machine learning, the application value of basketball pose recognition in physical education is becoming more and more extensive. This paper constructs a novel convolutional neural network model to recognize basketball posture. The model consists of 11 layers. Convolution and pooling operations are carried out for five basketball postures in the sampled data set. By fusing with the features extracted from HOG, finer features can be obtained. Finally, the data set is trained and recognized by entering the full connection layer for classification. The results show that compared with the traditional machine learning methods, the recognition performance of new model is better.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"6 1","pages":"12"},"PeriodicalIF":1.3,"publicationDate":"2022-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86111018","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 : 2022-01-01DOI: 10.4108/eai.6-10-2021.171248
Xiaojun Ma, Yuhua Qin, Dequan Kong, Desheng Liu, Chaoyang Wang
Aiming at the problem of poor adaptability and lag of traditional braking control methods of high-speed train, a high-speed train braking information fusion method based on adaptive linear auto disturbance rejection is proposed to arrange the transition process for accurate braking and stable operation of the train, and an extended state observer is designed to estimate and compensate the internal disturbance and external disturbance, so as to enhance the anti-interference ability of the system, By introducing adaptive control into linear ADRC, the real-time adaptive self-tuning of parameters is realized, the efficiency of parameter tuning is improved, and the problem that too many parameters have a direct impact on the control effect in ADRC is solved. The simulation results show that the control method can estimate and compensate the disturbance well, shows good robustness, and can track the ideal parking curve quickly and accurately.
{"title":"Adaptive and ADRC information fusion method for high speed train braking system","authors":"Xiaojun Ma, Yuhua Qin, Dequan Kong, Desheng Liu, Chaoyang Wang","doi":"10.4108/eai.6-10-2021.171248","DOIUrl":"https://doi.org/10.4108/eai.6-10-2021.171248","url":null,"abstract":"Aiming at the problem of poor adaptability and lag of traditional braking control methods of high-speed train, a high-speed train braking information fusion method based on adaptive linear auto disturbance rejection is proposed to arrange the transition process for accurate braking and stable operation of the train, and an extended state observer is designed to estimate and compensate the internal disturbance and external disturbance, so as to enhance the anti-interference ability of the system, By introducing adaptive control into linear ADRC, the real-time adaptive self-tuning of parameters is realized, the efficiency of parameter tuning is improved, and the problem that too many parameters have a direct impact on the control effect in ADRC is solved. The simulation results show that the control method can estimate and compensate the disturbance well, shows good robustness, and can track the ideal parking curve quickly and accurately.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"45 4","pages":"e9"},"PeriodicalIF":1.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72483605","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 : 2022-01-01DOI: 10.4108/eetcasa.v8i24.1920
A. A. Gizi
{"title":"Fuzzy Logic Control Design and Implementation with DC-DC Boost Converter","authors":"A. A. Gizi","doi":"10.4108/eetcasa.v8i24.1920","DOIUrl":"https://doi.org/10.4108/eetcasa.v8i24.1920","url":null,"abstract":"","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"42 1","pages":"e6"},"PeriodicalIF":1.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73714842","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 : 2022-01-01DOI: 10.4108/eai.21-3-2022.173712
Yiru Niu, Hong Jiang, Bo Tian, H. Xiang, Yiming Liu, Xiaofeng Xia, Yue Zhao
With the great concern of our country and the continuous development of the epidemic, the development of smart campus is getting faster and faster, the safety of teachers and students becomes more and more important. To ensure the safety of users, the first step is to control at the doors. Usually, the access control method is used in computer system to protect the documents and data, few people use it at doors, but it’s a very effective way to improve safety. So we design a two-factor authentication protocol to verity the user’s identity, and improve the attribute-based access control (ABAC) model to fit the smart campus. We analyze the protocol theoretically and verify its security. Compare with others, our scheme can be more efficient and safer.
{"title":"An efficient access control scheme for smart campus","authors":"Yiru Niu, Hong Jiang, Bo Tian, H. Xiang, Yiming Liu, Xiaofeng Xia, Yue Zhao","doi":"10.4108/eai.21-3-2022.173712","DOIUrl":"https://doi.org/10.4108/eai.21-3-2022.173712","url":null,"abstract":"With the great concern of our country and the continuous development of the epidemic, the development of smart campus is getting faster and faster, the safety of teachers and students becomes more and more important. To ensure the safety of users, the first step is to control at the doors. Usually, the access control method is used in computer system to protect the documents and data, few people use it at doors, but it’s a very effective way to improve safety. So we design a two-factor authentication protocol to verity the user’s identity, and improve the attribute-based access control (ABAC) model to fit the smart campus. We analyze the protocol theoretically and verify its security. Compare with others, our scheme can be more efficient and safer.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"1 1","pages":"e5"},"PeriodicalIF":1.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91334383","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 : 2021-12-17DOI: 10.4108/eai.17-12-2021.172438
Jiyue Wang, Xi Zhang, Yonggang Zhu
The traditional ciphertext retrieval scheme has some problems, such as low retrieval performance, lack of single keyword retrieval mode and limitation of single machine resources in traditional single server architecture. At the same time, for searchable encryption, it needs to balance the data security and retrieval efficiency. In this paper, a multi-keyword parallel ciphertext retrieval system based on inverted index is proposed. The system adopts different index encryption methods to improve the performance of ciphertext retrieval. Through the segmentation of ciphertext inverted index, the block retrieval of inverted index is realized, which overcomes the limitation of single machine resources and improves the retrieval efficiency. By combining the characteristics of distribution, the traditional single-machine retrieval architecture is extended and multi-keyword parallel retrieval is realized. The experimental results show that compared with SSE-1 scheme, the proposed scheme can improve the efficiency of retrieval, update and other operations on the premise of ensuring the security of ciphertext data, achieve multi-keyword retrieval, and dynamically expand the distributed architecture of the system. Finally, it can improve the system load capacity.
{"title":"A multi-keyword parallel ciphertext retrieval scheme based on inverted index under the robot distributed system","authors":"Jiyue Wang, Xi Zhang, Yonggang Zhu","doi":"10.4108/eai.17-12-2021.172438","DOIUrl":"https://doi.org/10.4108/eai.17-12-2021.172438","url":null,"abstract":"The traditional ciphertext retrieval scheme has some problems, such as low retrieval performance, lack of single keyword retrieval mode and limitation of single machine resources in traditional single server architecture. At the same time, for searchable encryption, it needs to balance the data security and retrieval efficiency. In this paper, a multi-keyword parallel ciphertext retrieval system based on inverted index is proposed. The system adopts different index encryption methods to improve the performance of ciphertext retrieval. Through the segmentation of ciphertext inverted index, the block retrieval of inverted index is realized, which overcomes the limitation of single machine resources and improves the retrieval efficiency. By combining the characteristics of distribution, the traditional single-machine retrieval architecture is extended and multi-keyword parallel retrieval is realized. The experimental results show that compared with SSE-1 scheme, the proposed scheme can improve the efficiency of retrieval, update and other operations on the premise of ensuring the security of ciphertext data, achieve multi-keyword retrieval, and dynamically expand the distributed architecture of the system. Finally, it can improve the system load capacity.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"6 1","pages":"e12"},"PeriodicalIF":1.3,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86059999","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 : 2021-12-17DOI: 10.4108/eai.17-12-2021.172439
Dianhuai Shen, X. Jiang, Lin Teng
Traditional image enhancement methods have the problems of low contrast and fuzzy details. Therefore, we propose a novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement. Firstly, multi-scale convolution is used to preprocess the image. Then, we improve the traditional Laplace edge detection operator and combine it with Gauss filter. The Gaussian filter is used to smooth the image and suppress the noise, and the edge detection is processed based on the Laplace gradient edge detector. The detail image extracted by Gauss-Laplace operator and the image with brightness enhancement are linearly weighted fused to reconstruct the image with clear detail edge and strong contrast. Experiments are carried out with detailed images in different scenes. It is compared with traditional methods to verify the effectiveness of the proposed method.
{"title":"A novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement","authors":"Dianhuai Shen, X. Jiang, Lin Teng","doi":"10.4108/eai.17-12-2021.172439","DOIUrl":"https://doi.org/10.4108/eai.17-12-2021.172439","url":null,"abstract":"Traditional image enhancement methods have the problems of low contrast and fuzzy details. Therefore, we propose a novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement. Firstly, multi-scale convolution is used to preprocess the image. Then, we improve the traditional Laplace edge detection operator and combine it with Gauss filter. The Gaussian filter is used to smooth the image and suppress the noise, and the edge detection is processed based on the Laplace gradient edge detector. The detail image extracted by Gauss-Laplace operator and the image with brightness enhancement are linearly weighted fused to reconstruct the image with clear detail edge and strong contrast. Experiments are carried out with detailed images in different scenes. It is compared with traditional methods to verify the effectiveness of the proposed method.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"5 1","pages":"e13"},"PeriodicalIF":1.3,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76646730","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 : 2021-12-07DOI: 10.4108/eai.7-12-2021.172362
Yian Xu
The application of building extraction involves a wide range of fields, including urban planning, land use analysis and change detection. It is difficult to determine whether each pixel is a building or not because of the large difference within the building category. Therefore, automatic building extraction from aerial images is still a challenging research topic. Although deep convolutional networks have many advantages, the networks used for image-level classification cannot be directly used for pixel-level building extraction tasks. This is caused by successive steps larger than one in the pooling or convolution layer. These operations will reduce the spatial resolution of feature maps. Therefore, the spatial resolution of the output feature map is no longer consistent with that of the input, which cannot meet the task requirements of pixel-level building extraction. In this paper, we propose a encoder-decoder structure based on conditional random field for building extraction in remote sensing images. The problem of boundary information lost by unitary potential energy in traditional conditional random field is solved through multi-scale building information. It also preserves the local structure information. The network consists of two parts: encoder sub-network and decoder sub-network. The encoder sub-network compresses the spatial resolution of the input image to complete the feature extraction. The decoder sub-network improves the spatial resolution from features and completes building extraction. Experimental results show that the proposed framework is superior to other comparison methods in terms of the accuracy on open data sets, and can extract building information in complex scenes well.
{"title":"Encoder-decoder structure based on conditional random field for building extraction in remote sensing images","authors":"Yian Xu","doi":"10.4108/eai.7-12-2021.172362","DOIUrl":"https://doi.org/10.4108/eai.7-12-2021.172362","url":null,"abstract":"The application of building extraction involves a wide range of fields, including urban planning, land use analysis and change detection. It is difficult to determine whether each pixel is a building or not because of the large difference within the building category. Therefore, automatic building extraction from aerial images is still a challenging research topic. Although deep convolutional networks have many advantages, the networks used for image-level classification cannot be directly used for pixel-level building extraction tasks. This is caused by successive steps larger than one in the pooling or convolution layer. These operations will reduce the spatial resolution of feature maps. Therefore, the spatial resolution of the output feature map is no longer consistent with that of the input, which cannot meet the task requirements of pixel-level building extraction. In this paper, we propose a encoder-decoder structure based on conditional random field for building extraction in remote sensing images. The problem of boundary information lost by unitary potential energy in traditional conditional random field is solved through multi-scale building information. It also preserves the local structure information. The network consists of two parts: encoder sub-network and decoder sub-network. The encoder sub-network compresses the spatial resolution of the input image to complete the feature extraction. The decoder sub-network improves the spatial resolution from features and completes building extraction. Experimental results show that the proposed framework is superior to other comparison methods in terms of the accuracy on open data sets, and can extract building information in complex scenes well.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"39 1","pages":"e9"},"PeriodicalIF":1.3,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74339527","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}
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