ABC v3 is a stream cipher submitted to the ECRYPT eStream project and has entered the second evaluation phase. Its key length is 128 bits. In this paper, we find large numbers of new weak keys of ABC family and introd...ABC v3 is a stream cipher submitted to the ECRYPT eStream project and has entered the second evaluation phase. Its key length is 128 bits. In this paper, we find large numbers of new weak keys of ABC family and introduce a method to search for them, and then apply a fast correlation attack to break ABC v3 with weak keys. We show that there are at least 2^103.71 new weak keys in ABC v3. Recovering the internal state of a weak key requires 236.05 keystream words and 2^50.56 operations. The attack can be applied to ABC vl and v2 with the same complexity as that of ABC v3. However, the number of weak keys of ABC vl as well as ABC v2 decreases to 2^97 ＋ 20^95.19,It reveals that ABC v3 incurs more weak keys than that of ABC vl and v2.展开更多
This paper provides a direct and fast acquisition algorithm of civilian long length（CL） codes in the L2 civil（L2C） signal. The proposed algorithm simultaneously reduces the number of fast Fourier transformation（...This paper provides a direct and fast acquisition algorithm of civilian long length（CL） codes in the L2 civil（L2C） signal. The proposed algorithm simultaneously reduces the number of fast Fourier transformation（FFT） correlation through hyper code technique and the amount of points in every FFT correlation by using an averaging correlation method. To validate the proposed acquisition performance, the paper applies this algorithm to the real L2C signal collected by the global positioning system（GPS） L2C intermediate frequency（IF） signal sampler—SIS100L2C. The acquisition results show that the proposed modified algorithm can acquire the code phase accurately with less calculation and its acquisition performance is better than the single hyper code method.展开更多
Air pollution is one of the major concerns considering detriments to human health.This type of pollution leads to several health problems for humans,such as asthma,heart issues,skin diseases,bronchitis,lung cancer,and...Air pollution is one of the major concerns considering detriments to human health.This type of pollution leads to several health problems for humans,such as asthma,heart issues,skin diseases,bronchitis,lung cancer,and throat and eye infections.Air pollution also poses serious issues to the planet.Pollution from the vehicle industry is the cause of greenhouse effect and CO2 emissions.Thus,real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary actions.The monitoring process has become efficient and dynamic with the advancement of the Internet of things and wireless sensor networks.Localization is the main issue in WSNs;if the sensor node location is unknown,then coverage and power and routing are not optimal.This study concentrates on localization-based air pollution prediction systems for real-time monitoring of smart cities.These systems comprise two phases considering the prediction as heavy or light traffic area using the Gaussian support vector machine algorithm based on the air pollutants,such as PM2.5 particulate matter,PM10,nitrogen dioxide(NO2),carbon monoxide(CO),ozone(O3),and sulfur dioxide(SO2).The sensor nodes are localized on the basis of the predicted area using the meta-heuristic algorithms called fast correlation-based elephant herding optimization.The dataset is divided into training and testing parts based on 10 cross-validations.The evaluation on predicting the air pollutant for localization is performed with the training dataset.Mean error prediction in localizing nodes is 9.83 which is lesser than existing solutions and accuracy is 95%.展开更多
基金the National Natural Science Foundation of China (Grant Nos.90604036 and 60525201)the 973 Project (Grant No.2007CB807902)
文摘ABC v3 is a stream cipher submitted to the ECRYPT eStream project and has entered the second evaluation phase. Its key length is 128 bits. In this paper, we find large numbers of new weak keys of ABC family and introduce a method to search for them, and then apply a fast correlation attack to break ABC v3 with weak keys. We show that there are at least 2^103.71 new weak keys in ABC v3. Recovering the internal state of a weak key requires 236.05 keystream words and 2^50.56 operations. The attack can be applied to ABC vl and v2 with the same complexity as that of ABC v3. However, the number of weak keys of ABC vl as well as ABC v2 decreases to 2^97 ＋ 20^95.19,It reveals that ABC v3 incurs more weak keys than that of ABC vl and v2.
基金supported by the Fundamental Research Fund for the Central Universities(NS2013016)
文摘This paper provides a direct and fast acquisition algorithm of civilian long length（CL） codes in the L2 civil（L2C） signal. The proposed algorithm simultaneously reduces the number of fast Fourier transformation（FFT） correlation through hyper code technique and the amount of points in every FFT correlation by using an averaging correlation method. To validate the proposed acquisition performance, the paper applies this algorithm to the real L2C signal collected by the global positioning system（GPS） L2C intermediate frequency（IF） signal sampler—SIS100L2C. The acquisition results show that the proposed modified algorithm can acquire the code phase accurately with less calculation and its acquisition performance is better than the single hyper code method.
基金The authors would like to acknowledge the support of Taif UniversityResearchers Supporting Project number (TURSP-2020/10), Taif University, Taif, Saudi Arabia.
文摘Air pollution is one of the major concerns considering detriments to human health.This type of pollution leads to several health problems for humans,such as asthma,heart issues,skin diseases,bronchitis,lung cancer,and throat and eye infections.Air pollution also poses serious issues to the planet.Pollution from the vehicle industry is the cause of greenhouse effect and CO2 emissions.Thus,real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary actions.The monitoring process has become efficient and dynamic with the advancement of the Internet of things and wireless sensor networks.Localization is the main issue in WSNs;if the sensor node location is unknown,then coverage and power and routing are not optimal.This study concentrates on localization-based air pollution prediction systems for real-time monitoring of smart cities.These systems comprise two phases considering the prediction as heavy or light traffic area using the Gaussian support vector machine algorithm based on the air pollutants,such as PM2.5 particulate matter,PM10,nitrogen dioxide(NO2),carbon monoxide(CO),ozone(O3),and sulfur dioxide(SO2).The sensor nodes are localized on the basis of the predicted area using the meta-heuristic algorithms called fast correlation-based elephant herding optimization.The dataset is divided into training and testing parts based on 10 cross-validations.The evaluation on predicting the air pollutant for localization is performed with the training dataset.Mean error prediction in localizing nodes is 9.83 which is lesser than existing solutions and accuracy is 95%.
