Secret key generation(SKG)is a promising solution to the problem of wireless communications security.As the first step of SKG,channel probing affects it significantly.Although there have been some probing schemes,ther...Secret key generation(SKG)is a promising solution to the problem of wireless communications security.As the first step of SKG,channel probing affects it significantly.Although there have been some probing schemes,there is a lack of research on the optimization of the probing process.This study investigates how to optimize correlated parameters to maximize the SKG rate(SKGR)in the time-division duplex(TDD)mode.First,we build a probing model which includes the effects of transmitting power,the probing period,and the dimension of sample vectors.Based on the model,the analytical expression of the SKGR is given.Next,we formulate an optimization problem for maximizing the SKGR and give an algorithm to solve it.We conclude the SKGR monotonically increases as the transmitting power increases.Relevant mathematical proofs are given in this study.From the simulation results,increasing appropriately the probing period and the dimension of the sample vector could increase the SKGR dramatically compared to a yardstick,which indicates the importance of optimizing the parameters related to the channel probing phase.展开更多
In developing counties, efficiency of economic development has been determined by the analysis of industrial production. An examination of the characteristic of industrial sector is an essential aspect of growth studi...In developing counties, efficiency of economic development has been determined by the analysis of industrial production. An examination of the characteristic of industrial sector is an essential aspect of growth studies. The growth of a country can be measured by Gross Domestic Product (GDP). GDP is substantially affected by the industrial output. Industrial gross output is mainly a function of capital and labor input. If the effect of labor and capital input to output is at a satisfactory level in an industry or in a group of industries, then industrial investment will increase. As a result, the number of industries will increase, which will directly affect GDP and also will decrease the unemployment rate. This is why, industrial input-output relationship is so important for any industry as well as for the overall industrial sector of a country. To forecast the production of a firm is necessary to identify the appropriate model. MD. M. Hossain et al. [1] have shown that Cobb-Douglas production function with additive errors was more suitable for some selected manufacturing industries in Bangladesh. The main purpose of this paper is to detect the autocorrelation problem of Cobb-Douglas production model with additive errors. The result shows that autocorrelation is presented in some manufacturing industries. Finally, this paper removes the autocorrelation problem and re-estimates the parameters of the Cobb- Douglas production function with additive errors.展开更多
In this study, we regard written texts as time series data and try to investigate dynamic correlations of word occurrences by utilizing an autocorrelation function (ACF). After defining appropriate formula for the ACF...In this study, we regard written texts as time series data and try to investigate dynamic correlations of word occurrences by utilizing an autocorrelation function (ACF). After defining appropriate formula for the ACF that is suitable for expressing the dynamic correlations of words, we use the formula to calculate ACFs for frequent words in 12 books. The ACFs obtained can be classified into two groups: One group of ACFs shows dynamic correlations, with these ACFs well described by a modified Kohlrausch-Williams-Watts (KWW) function;the other group of ACFs shows no correlations, with these ACFs fitted by a simple stepdown function. A word having the former ACF is called a Type-I word and a word with the latter ACF is called a Type-II word. It is also shown that the ACFs of Type-II words can be derived theoretically by assuming that the stochastic process governing word occurrence is a homogeneous Poisson point process. Based on the fitting of the ACFs by KWW and stepdown functions, we propose a measure of word importance which expresses the extent to which a word is important in a particular text. The validity of the measure is confirmed by using the Kleinburg’s burst detection algorithm.展开更多
Cryptographic properties of the single cycle T-function's output sequences are investigated.Bounds of autocorrelation functions of the kth coordinate sequence and bounds of state output sequence are calculated res...Cryptographic properties of the single cycle T-function's output sequences are investigated.Bounds of autocorrelation functions of the kth coordinate sequence and bounds of state output sequence are calculated respectively.The Maximum Sidelobe Ratio(MSR) of the kth coordinate sequence and the MSR of state output sequence are given respectively.The bounds of autocorrelation functions show that the values of autocorrelation functions are large when shifts are small.Comparisons of the autocorrelations between the state output sequence and coordinate output sequence are illustrated.The autocorrelation properties demonstrate that T-functions have cryptographic weaknesses and the illustration result shows coordinate output sequences have better autocorrelation than that of state output sequences.展开更多
This paper proposes a desirable method to detect different kinds of low probability of intercept (LPI) radar signals, targeted at the main intra-pulse modulation method of LPI radar signals including the signals of li...This paper proposes a desirable method to detect different kinds of low probability of intercept (LPI) radar signals, targeted at the main intra-pulse modulation method of LPI radar signals including the signals of linear frequency modulation, phase code, and frequency code. Firstly, it improves the coherent integration of LPI radar signals by adding the periodicity of the ambiguity function. Then, it develops a frequency domain detection method based on fast Fourier transform (FFT) and segmented autocorrelation function to detect signals without features of linear frequency modulation by virtue of the distribution characteristics of noise signals in the frequency domain. Finally, this paper gives a verification of the performance of the method for different signal-to-noise ratios by conducting simulation experiments, and compares the method with existing ones. Additionally, this method is characterized by the straightforward calculation and high real-time performance, which is conducive to better detecting all kinds of LPI radar signals.展开更多
Considering the calculated result and higher degeneracy existing in the calculation of autocorrelation topological index totally depend on experimental parameters, a new group of autocorrelation topological index as ...Considering the calculated result and higher degeneracy existing in the calculation of autocorrelation topological index totally depend on experimental parameters, a new group of autocorrelation topological index as A t, B t, C t and D t was designed and developed based on the vertex degree of molecular topology and autocorrelation function of mathematics. Autocorrelation function f(i) was calculated from the square root of the vertex degree, revised vertex degree and their combination, and they are (δ i) 1/2 , (δ V i) 1/2 ,(δ V i+δ i) 1/2 and (δ E i-δ i) 1/2 / N. With the matrix description method achieved, and the unit input in matrix of unsaturated bond and heteroatoms revised based on the adjacency matrix and distance matrix of organic molecular graph, the corresponding computer software has also been designed and developed. Better results have been obtained for the application of these indexes in QSAR study of organic chemicals.展开更多
Computational electrocardiogram (ECG) analysis is one of the most crucial topics in cardiovascular research domain especially in identifying abnormalities of heart condition through cardiac arrhythmia symptom. There a...Computational electrocardiogram (ECG) analysis is one of the most crucial topics in cardiovascular research domain especially in identifying abnormalities of heart condition through cardiac arrhythmia symptom. There are many existing works focusing on recognizing the abnormalities condition through arrhythmia symptom, however, the detection rate is still unsatisfied. Arrhythmia consists of more than 14 various types of symptoms. Therefore, most of the existing research found it difficult to classify the entire symptom and maintain the overall accuracy especially in long hour data. In this study, a new mechanism to overcome this issue is proposed: A combination between Autocorrelation methods with K-Nearest Neighbor (KNN) classifier method is introduced to accurately and robustly detect 14 types of Arrhythmia symptom regardless of the origin of the symptom in a long hour data. Moreover, variability analysis based on periodic autocorrelation result is proposed and used for classification procedure. 1 minute and 12 hours duration data was chosen to compare and signify the most suitable time duration to detect Arrhythmia symptom. In addition, an analytical result and discussion is done to provide justification behind each tendency of Arrhythmia and Normal Sinus symptom in autocorrelation result. As the result of proposed method performance evaluation, it was revealed that the accuracy of 95.5% in discriminating Arrhythmia from Normal Sinus data is achieved. Furthermore, it was confirmed that utilizing autocorrelation result in long hour data can help to generalize abnormalities characteristic of heart condition like Arrhythmia symptom. It is concluded that the proposed method can be useful to diagnose abnormalities of heart condition at any stage.展开更多
基金supported in part by the national natural science foundation of China (NSFC) under Grant61871193in part by the R&D Program of key science and technology fields in Guangdong province under Grant 2019B090912001in part by the Guangzhou Key Field R&D Program under Grant 202206030005
文摘Secret key generation(SKG)is a promising solution to the problem of wireless communications security.As the first step of SKG,channel probing affects it significantly.Although there have been some probing schemes,there is a lack of research on the optimization of the probing process.This study investigates how to optimize correlated parameters to maximize the SKG rate(SKGR)in the time-division duplex(TDD)mode.First,we build a probing model which includes the effects of transmitting power,the probing period,and the dimension of sample vectors.Based on the model,the analytical expression of the SKGR is given.Next,we formulate an optimization problem for maximizing the SKGR and give an algorithm to solve it.We conclude the SKGR monotonically increases as the transmitting power increases.Relevant mathematical proofs are given in this study.From the simulation results,increasing appropriately the probing period and the dimension of the sample vector could increase the SKGR dramatically compared to a yardstick,which indicates the importance of optimizing the parameters related to the channel probing phase.
文摘In developing counties, efficiency of economic development has been determined by the analysis of industrial production. An examination of the characteristic of industrial sector is an essential aspect of growth studies. The growth of a country can be measured by Gross Domestic Product (GDP). GDP is substantially affected by the industrial output. Industrial gross output is mainly a function of capital and labor input. If the effect of labor and capital input to output is at a satisfactory level in an industry or in a group of industries, then industrial investment will increase. As a result, the number of industries will increase, which will directly affect GDP and also will decrease the unemployment rate. This is why, industrial input-output relationship is so important for any industry as well as for the overall industrial sector of a country. To forecast the production of a firm is necessary to identify the appropriate model. MD. M. Hossain et al. [1] have shown that Cobb-Douglas production function with additive errors was more suitable for some selected manufacturing industries in Bangladesh. The main purpose of this paper is to detect the autocorrelation problem of Cobb-Douglas production model with additive errors. The result shows that autocorrelation is presented in some manufacturing industries. Finally, this paper removes the autocorrelation problem and re-estimates the parameters of the Cobb- Douglas production function with additive errors.
文摘In this paper, we obtain an explicit expression for the partial autocorrelation of an ARMA (1.1) process and discuss its asymptotic behaviour briefly.
文摘In this study, we regard written texts as time series data and try to investigate dynamic correlations of word occurrences by utilizing an autocorrelation function (ACF). After defining appropriate formula for the ACF that is suitable for expressing the dynamic correlations of words, we use the formula to calculate ACFs for frequent words in 12 books. The ACFs obtained can be classified into two groups: One group of ACFs shows dynamic correlations, with these ACFs well described by a modified Kohlrausch-Williams-Watts (KWW) function;the other group of ACFs shows no correlations, with these ACFs fitted by a simple stepdown function. A word having the former ACF is called a Type-I word and a word with the latter ACF is called a Type-II word. It is also shown that the ACFs of Type-II words can be derived theoretically by assuming that the stochastic process governing word occurrence is a homogeneous Poisson point process. Based on the fitting of the ACFs by KWW and stepdown functions, we propose a measure of word importance which expresses the extent to which a word is important in a particular text. The validity of the measure is confirmed by using the Kleinburg’s burst detection algorithm.
基金supported by National Natural Science Foundation of China under Grant No.60833008,60970119the Scientific Research Foundation of Education of Department of Shaanxi Provincial Government of China under Grant No.11JK0503+1 种基金Youth Science and Technology Foundation of Xi'an University of Architecture and Technology under Grant No.QN0831,QN1024Foundation of Guangxi Key Laboratory of Information and Communications under Grant No.20902
文摘Cryptographic properties of the single cycle T-function's output sequences are investigated.Bounds of autocorrelation functions of the kth coordinate sequence and bounds of state output sequence are calculated respectively.The Maximum Sidelobe Ratio(MSR) of the kth coordinate sequence and the MSR of state output sequence are given respectively.The bounds of autocorrelation functions show that the values of autocorrelation functions are large when shifts are small.Comparisons of the autocorrelations between the state output sequence and coordinate output sequence are illustrated.The autocorrelation properties demonstrate that T-functions have cryptographic weaknesses and the illustration result shows coordinate output sequences have better autocorrelation than that of state output sequences.
基金supported by the National Natural Science Foundation of China(61571462)Weapons and Equipment Exploration Research Project(7131464)
文摘This paper proposes a desirable method to detect different kinds of low probability of intercept (LPI) radar signals, targeted at the main intra-pulse modulation method of LPI radar signals including the signals of linear frequency modulation, phase code, and frequency code. Firstly, it improves the coherent integration of LPI radar signals by adding the periodicity of the ambiguity function. Then, it develops a frequency domain detection method based on fast Fourier transform (FFT) and segmented autocorrelation function to detect signals without features of linear frequency modulation by virtue of the distribution characteristics of noise signals in the frequency domain. Finally, this paper gives a verification of the performance of the method for different signal-to-noise ratios by conducting simulation experiments, and compares the method with existing ones. Additionally, this method is characterized by the straightforward calculation and high real-time performance, which is conducive to better detecting all kinds of LPI radar signals.
文摘Considering the calculated result and higher degeneracy existing in the calculation of autocorrelation topological index totally depend on experimental parameters, a new group of autocorrelation topological index as A t, B t, C t and D t was designed and developed based on the vertex degree of molecular topology and autocorrelation function of mathematics. Autocorrelation function f(i) was calculated from the square root of the vertex degree, revised vertex degree and their combination, and they are (δ i) 1/2 , (δ V i) 1/2 ,(δ V i+δ i) 1/2 and (δ E i-δ i) 1/2 / N. With the matrix description method achieved, and the unit input in matrix of unsaturated bond and heteroatoms revised based on the adjacency matrix and distance matrix of organic molecular graph, the corresponding computer software has also been designed and developed. Better results have been obtained for the application of these indexes in QSAR study of organic chemicals.
文摘Computational electrocardiogram (ECG) analysis is one of the most crucial topics in cardiovascular research domain especially in identifying abnormalities of heart condition through cardiac arrhythmia symptom. There are many existing works focusing on recognizing the abnormalities condition through arrhythmia symptom, however, the detection rate is still unsatisfied. Arrhythmia consists of more than 14 various types of symptoms. Therefore, most of the existing research found it difficult to classify the entire symptom and maintain the overall accuracy especially in long hour data. In this study, a new mechanism to overcome this issue is proposed: A combination between Autocorrelation methods with K-Nearest Neighbor (KNN) classifier method is introduced to accurately and robustly detect 14 types of Arrhythmia symptom regardless of the origin of the symptom in a long hour data. Moreover, variability analysis based on periodic autocorrelation result is proposed and used for classification procedure. 1 minute and 12 hours duration data was chosen to compare and signify the most suitable time duration to detect Arrhythmia symptom. In addition, an analytical result and discussion is done to provide justification behind each tendency of Arrhythmia and Normal Sinus symptom in autocorrelation result. As the result of proposed method performance evaluation, it was revealed that the accuracy of 95.5% in discriminating Arrhythmia from Normal Sinus data is achieved. Furthermore, it was confirmed that utilizing autocorrelation result in long hour data can help to generalize abnormalities characteristic of heart condition like Arrhythmia symptom. It is concluded that the proposed method can be useful to diagnose abnormalities of heart condition at any stage.
