Aiming at the higher bit-rate occupation of motion vector encoding and more time load of full-searching strategies, a multi-resolution motion estimation and compensation algorithm based on adjacent prediction of frame...Aiming at the higher bit-rate occupation of motion vector encoding and more time load of full-searching strategies, a multi-resolution motion estimation and compensation algorithm based on adjacent prediction of frame difference was proposed.Differential motion detection was employed to image sequences and proper threshold was adopted to identify the connected region.Then the motion region was extracted to carry out motion estimation and motion compensation on it.The experiment results show that the encoding efficiency of motion vector is promoted, the complexity of motion estimation is reduced and the quality of the reconstruction image at the same bit-rate as Multi-Resolution Motion Estimation(MRME) is improved.展开更多
The homomorphic hash algorithm(HHA)is introduced to help on-the-fly verify the vireless sensor network(WSN)over-the-air programming(OAP)data based on rateless codes.The receiver calculates the hash value of a group of...The homomorphic hash algorithm(HHA)is introduced to help on-the-fly verify the vireless sensor network(WSN)over-the-air programming(OAP)data based on rateless codes.The receiver calculates the hash value of a group of data by homomorphic hash function,and then it compares the hash value with the receiving message digest.Because the feedback channel is deliberately removed during the distribution process,the rateless codes are often vulnerable when they face security issues such as packets contamination or attack.This method prevents contaminating or attack on rateless codes and reduces the potential risks of decoding failure.Compared with the SHA1 and MD5,HHA,which has a much shorter message digest,will deliver more data.The simulation results show that to transmit and verify the same amount of OAP data,HHA method sends 17.9% to 23.1%fewer packets than MD5 and SHA1 under different packet loss rates.展开更多
In recent years,with the massive growth of image data,how to match the image required by users quickly and efficiently becomes a challenge.Compared with single-view feature,multi-view feature is more accurate to descr...In recent years,with the massive growth of image data,how to match the image required by users quickly and efficiently becomes a challenge.Compared with single-view feature,multi-view feature is more accurate to describe image information.The advantages of hash method in reducing data storage and improving efficiency also make us study how to effectively apply to large-scale image retrieval.In this paper,a hash algorithm of multi-index image retrieval based on multi-view feature coding is proposed.By learning the data correlation between different views,this algorithm uses multi-view data with deeper level image semantics to achieve better retrieval results.This algorithm uses a quantitative hash method to generate binary sequences,and uses the hash code generated by the association features to construct database inverted index files,so as to reduce the memory burden and promote the efficient matching.In order to reduce the matching error of hash code and ensure the retrieval accuracy,this algorithm uses inverted multi-index structure instead of single-index structure.Compared with other advanced image retrieval method,this method has better retrieval performance.展开更多
A novel hashing method based on multiple heterogeneous features is proposed to improve the accuracy of the image retrieval system. First, it leverages the imbalanced distribution of the similar and dissimilar samples ...A novel hashing method based on multiple heterogeneous features is proposed to improve the accuracy of the image retrieval system. First, it leverages the imbalanced distribution of the similar and dissimilar samples in the feature space to boost the performance of each weak classifier in the asymmetric boosting framework. Then, the weak classifier based on a novel linear discriminate analysis （LDA） algorithm which is learned from the subspace of heterogeneous features is integrated into the framework. Finally, the proposed method deals with each bit of the code sequentially, which utilizes the samples misclassified in each round in order to learn compact and balanced code. The heterogeneous information from different modalities can be effectively complementary to each other, which leads to much higher performance. The experimental results based on the two public benchmarks demonstrate that this method is superior to many of the state- of-the-art methods. In conclusion, the performance of the retrieval system can be improved with the help of multiple heterogeneous features and the compact hash codes which can be learned by the imbalanced learning method.展开更多
Wireless Network security management is difficult because of the ever-increasing number of wireless network malfunctions,vulnerabilities,and assaults.Complex security systems,such as Intrusion Detection Systems(IDS),a...Wireless Network security management is difficult because of the ever-increasing number of wireless network malfunctions,vulnerabilities,and assaults.Complex security systems,such as Intrusion Detection Systems(IDS),are essential due to the limitations of simpler security measures,such as cryptography and firewalls.Due to their compact nature and low energy reserves,wireless networks present a significant challenge for security procedures.The features of small cells can cause threats to the network.Network Coding(NC)enabled small cells are vulnerable to various types of attacks.Avoiding attacks and performing secure“peer”to“peer”data transmission is a challenging task in small cells.Due to the low power and memory requirements of the proposed model,it is well suited to use with constrained small cells.An attacker cannot change the contents of data and generate a new Hashed Homomorphic Message Authentication Code(HHMAC)hash between transmissions since the HMAC function is generated using the shared secret.In this research,a chaotic sequence mapping based low overhead 1D Improved Logistic Map is used to secure“peer”to“peer”data transmission model using lightweight H-MAC(1D-LM-P2P-LHHMAC)is proposed with accurate intrusion detection.The proposed model is evaluated with the traditional models by considering various evaluation metrics like Vector Set Generation Accuracy Levels,Key Pair Generation Time Levels,Chaotic Map Accuracy Levels,Intrusion Detection Accuracy Levels,and the results represent that the proposed model performance in chaotic map accuracy level is 98%and intrusion detection is 98.2%.The proposed model is compared with the traditional models and the results represent that the proposed model secure data transmission levels are high.展开更多
传统的ACF+AdaBoost行人检测框架在达到较为理想的检测率时,误检率也会迅速增高,难以满足实际需求。针对该问题,本文提出了一种自适应加权的Hash码特征,用来增加行人特征的多样性。在此基础上,通过级联一个辅助网络降低系统的误检率,该...传统的ACF+AdaBoost行人检测框架在达到较为理想的检测率时,误检率也会迅速增高,难以满足实际需求。针对该问题,本文提出了一种自适应加权的Hash码特征,用来增加行人特征的多样性。在此基础上,通过级联一个辅助网络降低系统的误检率,该辅助网络采用了浅层的CNN结构,在保证系统实时性的前提下对AdaBoost分类器的分类结果进行二次分类。在INRIA数据中进行检测实验的结果表明,改进的Hash码简单、易算,对行人的表征能力强,在不影响实时性的前提下,把系统的MR-FPPI(Miss rate against false positives per image)从17.05%降低到16.31%。系统级联辅助CNN后系统的MR-FPPI降低到16.93%,而加入Hash码通道,且级联辅助CNN后,系统的MR-FPPI降低到15.96%,检测性能得到较为明显的提高。展开更多
基金Supported by the National Natural Science Foundation of China (No. 60803036)the Scientific Research Fund of Heilongjiang Provincial Education Department (No.11531013)
文摘Aiming at the higher bit-rate occupation of motion vector encoding and more time load of full-searching strategies, a multi-resolution motion estimation and compensation algorithm based on adjacent prediction of frame difference was proposed.Differential motion detection was employed to image sequences and proper threshold was adopted to identify the connected region.Then the motion region was extracted to carry out motion estimation and motion compensation on it.The experiment results show that the encoding efficiency of motion vector is promoted, the complexity of motion estimation is reduced and the quality of the reconstruction image at the same bit-rate as Multi-Resolution Motion Estimation(MRME) is improved.
基金Supported by the National Science and Technology Support Program(Y2140161A5)the National High Technology Research and Development Program of China(863Program)(O812041A04)
文摘The homomorphic hash algorithm(HHA)is introduced to help on-the-fly verify the vireless sensor network(WSN)over-the-air programming(OAP)data based on rateless codes.The receiver calculates the hash value of a group of data by homomorphic hash function,and then it compares the hash value with the receiving message digest.Because the feedback channel is deliberately removed during the distribution process,the rateless codes are often vulnerable when they face security issues such as packets contamination or attack.This method prevents contaminating or attack on rateless codes and reduces the potential risks of decoding failure.Compared with the SHA1 and MD5,HHA,which has a much shorter message digest,will deliver more data.The simulation results show that to transmit and verify the same amount of OAP data,HHA method sends 17.9% to 23.1%fewer packets than MD5 and SHA1 under different packet loss rates.
基金supported in part by the National Natural Science Foundation of China under Grant 61772561,author J.Q,http://www.nsfc.gov.cn/in part by the Key Research and Development Plan of Hunan Province under Grant 2018NK2012,author J.Q,http://kjt.hunan.gov.cn/+7 种基金in part by the Key Research and Development Plan of Hunan Province under Grant 2019SK2022,author Y.T,http://kjt.hunan.gov.cn/in part by the Science Research Projects of Hunan Provincial Education Department under Grant 18A174,author X.X,http://kxjsc.gov.hnedu.cn/in part by the Science Research Projects of Hunan Provincial Education Department under Grant 19B584,author Y.T,http://kxjsc.gov.hnedu.cn/in part by the Degree&Postgraduate Education Reform Project of Hunan Province under Grant 2019JGYB154,author J.Q,http://xwb.gov.hnedu.cn/in part by the Postgraduate Excellent teaching team Project of Hunan Province under Grant[2019]370-133,author J.Q,http://xwb.gov.hnedu.cn/in part by the Postgraduate Education and Teaching Reform Project of Central South University of Forestry&Technology under Grant 2019JG013,author X.X,http://jwc.csuft.edu.cn/in part by the Natural Science Foundation of Hunan Province(No.2020JJ4140),author Y.T,http://kjt.hunan.gov.cn/in part by the Natural Science Foundation of Hunan Province(No.2020JJ4141),author X.X,http://kjt.hunan.gov.cn/.
文摘In recent years,with the massive growth of image data,how to match the image required by users quickly and efficiently becomes a challenge.Compared with single-view feature,multi-view feature is more accurate to describe image information.The advantages of hash method in reducing data storage and improving efficiency also make us study how to effectively apply to large-scale image retrieval.In this paper,a hash algorithm of multi-index image retrieval based on multi-view feature coding is proposed.By learning the data correlation between different views,this algorithm uses multi-view data with deeper level image semantics to achieve better retrieval results.This algorithm uses a quantitative hash method to generate binary sequences,and uses the hash code generated by the association features to construct database inverted index files,so as to reduce the memory burden and promote the efficient matching.In order to reduce the matching error of hash code and ensure the retrieval accuracy,this algorithm uses inverted multi-index structure instead of single-index structure.Compared with other advanced image retrieval method,this method has better retrieval performance.
基金The National Natural Science Foundation of China(No.61305058)the Natural Science Foundation of Higher Education Institutions of Jiangsu Province(No.12KJB520003)+1 种基金the Natural Science Foundation of Jiangsu Province(No.BK20130471)the Scientific Research Foundation for Advanced Talents by Jiangsu University(No.13JDG093)
文摘A novel hashing method based on multiple heterogeneous features is proposed to improve the accuracy of the image retrieval system. First, it leverages the imbalanced distribution of the similar and dissimilar samples in the feature space to boost the performance of each weak classifier in the asymmetric boosting framework. Then, the weak classifier based on a novel linear discriminate analysis （LDA） algorithm which is learned from the subspace of heterogeneous features is integrated into the framework. Finally, the proposed method deals with each bit of the code sequentially, which utilizes the samples misclassified in each round in order to learn compact and balanced code. The heterogeneous information from different modalities can be effectively complementary to each other, which leads to much higher performance. The experimental results based on the two public benchmarks demonstrate that this method is superior to many of the state- of-the-art methods. In conclusion, the performance of the retrieval system can be improved with the help of multiple heterogeneous features and the compact hash codes which can be learned by the imbalanced learning method.
文摘Wireless Network security management is difficult because of the ever-increasing number of wireless network malfunctions,vulnerabilities,and assaults.Complex security systems,such as Intrusion Detection Systems(IDS),are essential due to the limitations of simpler security measures,such as cryptography and firewalls.Due to their compact nature and low energy reserves,wireless networks present a significant challenge for security procedures.The features of small cells can cause threats to the network.Network Coding(NC)enabled small cells are vulnerable to various types of attacks.Avoiding attacks and performing secure“peer”to“peer”data transmission is a challenging task in small cells.Due to the low power and memory requirements of the proposed model,it is well suited to use with constrained small cells.An attacker cannot change the contents of data and generate a new Hashed Homomorphic Message Authentication Code(HHMAC)hash between transmissions since the HMAC function is generated using the shared secret.In this research,a chaotic sequence mapping based low overhead 1D Improved Logistic Map is used to secure“peer”to“peer”data transmission model using lightweight H-MAC(1D-LM-P2P-LHHMAC)is proposed with accurate intrusion detection.The proposed model is evaluated with the traditional models by considering various evaluation metrics like Vector Set Generation Accuracy Levels,Key Pair Generation Time Levels,Chaotic Map Accuracy Levels,Intrusion Detection Accuracy Levels,and the results represent that the proposed model performance in chaotic map accuracy level is 98%and intrusion detection is 98.2%.The proposed model is compared with the traditional models and the results represent that the proposed model secure data transmission levels are high.
文摘传统的ACF+AdaBoost行人检测框架在达到较为理想的检测率时,误检率也会迅速增高,难以满足实际需求。针对该问题,本文提出了一种自适应加权的Hash码特征,用来增加行人特征的多样性。在此基础上,通过级联一个辅助网络降低系统的误检率,该辅助网络采用了浅层的CNN结构,在保证系统实时性的前提下对AdaBoost分类器的分类结果进行二次分类。在INRIA数据中进行检测实验的结果表明,改进的Hash码简单、易算,对行人的表征能力强,在不影响实时性的前提下,把系统的MR-FPPI(Miss rate against false positives per image)从17.05%降低到16.31%。系统级联辅助CNN后系统的MR-FPPI降低到16.93%,而加入Hash码通道,且级联辅助CNN后,系统的MR-FPPI降低到15.96%,检测性能得到较为明显的提高。
