ISSN 
2017 Vol. 46, No. 5
2017, 46(5): 641-647.
doi: 10.3969/j.issn.1001-0548.2017.05.001
Abstract:
Low-density parity-check (LDPC) codes are a class of linear block codes defined by the check matrix, which have the error-correcting capability of approaching Shannon limit. Based on the symmetric cryptosystem with error correcting codes as well as performance equivalent coding matrix, this paper proposes a secure communication method based on LDPC codes. This method greatly improves the anti-intercept capability of system and keeps the reliability almost unchanged by constructing a large number of performance equivalent coding matrixes and by simultaneously and randomly shifting the coding matrixes by the sender and receiver. Thus the proposed method has better error-correcting capability and can be applied as a secure and reliable integrated communication solution.
Low-density parity-check (LDPC) codes are a class of linear block codes defined by the check matrix, which have the error-correcting capability of approaching Shannon limit. Based on the symmetric cryptosystem with error correcting codes as well as performance equivalent coding matrix, this paper proposes a secure communication method based on LDPC codes. This method greatly improves the anti-intercept capability of system and keeps the reliability almost unchanged by constructing a large number of performance equivalent coding matrixes and by simultaneously and randomly shifting the coding matrixes by the sender and receiver. Thus the proposed method has better error-correcting capability and can be applied as a secure and reliable integrated communication solution.
2017, 46(5): 648-653.
doi: 10.3969/j.issn.1001-0548.2017.05.002
Abstract:
Power consumption is a key issue in digital circuit design. The reliability of circuits becomes one of main challenges for low supply voltage design. Markov random field (MRF) circuits, which are the probabilistic-based approaches with energy based point of view, can achieve high noise immunity in ultra-low supply. However the traditional MRF elements have complex structures which become a stringent limitation factor for the application of MRF-based circuits in VLSI design. In this paper, we present a partial MRF (PMRF) clique energy design method for multi-logic elements, which can be referred to complementary PMRF pair. The proposed structure compensates the performance loss and achieves the area and complexity reduction. A full carry-look-ahead adder is implemented by using our proposed PMRF-pairs on the 65 nm TSMC CMOS technology. The measurement results show that the PMRF-pairs design can achieve higher fault-tolerance while occupying 29% area-saving, 86% energy-saving and 20% performance improvement compared with the complete MRF design.
Power consumption is a key issue in digital circuit design. The reliability of circuits becomes one of main challenges for low supply voltage design. Markov random field (MRF) circuits, which are the probabilistic-based approaches with energy based point of view, can achieve high noise immunity in ultra-low supply. However the traditional MRF elements have complex structures which become a stringent limitation factor for the application of MRF-based circuits in VLSI design. In this paper, we present a partial MRF (PMRF) clique energy design method for multi-logic elements, which can be referred to complementary PMRF pair. The proposed structure compensates the performance loss and achieves the area and complexity reduction. A full carry-look-ahead adder is implemented by using our proposed PMRF-pairs on the 65 nm TSMC CMOS technology. The measurement results show that the PMRF-pairs design can achieve higher fault-tolerance while occupying 29% area-saving, 86% energy-saving and 20% performance improvement compared with the complete MRF design.
2017, 46(5): 654-659, 665.
doi: 10.3969/j.issn.1001-0548.2017.05.003
Abstract:
The main challenges of indoor localization come from multi-path propagation and non-stationary channel environment. Some classical localization approaches based on single received signal strength (RSS) fingerprint show low accuracy and bad robustness due to some environment changes. In this paper, we propose an accurate indoor localization algorithm by fusing group of fingerprints via Dampster-Shafer (D-S) evidence theory. The main idea can be summarized as follows:in off-line phase, first, based on the received data from a receiving array deployed in indoor environment, we calculate four fingerprints, namely, RSS, covariance matrix, signal subspace, and fourth-order cumulant. Secondly, these fingerprints are input to train four different classifiers by using back-propagation (BP) neural networks. In on-line phase, by calculating the corresponding transformations of the received signals of the array, we can obtain the predictions of these classifiers; then, we use D-S evidence theory to fuse the final localization results. The proposed algorithm can deal with different environment noise adaptively and show higher accuracy compared with some existing fingerprint-based algorithms. The performance of our proposed algorithm is verified by simulation results.
The main challenges of indoor localization come from multi-path propagation and non-stationary channel environment. Some classical localization approaches based on single received signal strength (RSS) fingerprint show low accuracy and bad robustness due to some environment changes. In this paper, we propose an accurate indoor localization algorithm by fusing group of fingerprints via Dampster-Shafer (D-S) evidence theory. The main idea can be summarized as follows:in off-line phase, first, based on the received data from a receiving array deployed in indoor environment, we calculate four fingerprints, namely, RSS, covariance matrix, signal subspace, and fourth-order cumulant. Secondly, these fingerprints are input to train four different classifiers by using back-propagation (BP) neural networks. In on-line phase, by calculating the corresponding transformations of the received signals of the array, we can obtain the predictions of these classifiers; then, we use D-S evidence theory to fuse the final localization results. The proposed algorithm can deal with different environment noise adaptively and show higher accuracy compared with some existing fingerprint-based algorithms. The performance of our proposed algorithm is verified by simulation results.
2017, 46(5): 660-665.
doi: 10.3969/j.issn.1001-0548.2017.05.004
Abstract:
Aiming at the problems of large amount of information exchange, slow convergence and unstable convergence result of the original whole-network distributed cooperative consensus method, a distributed consensus cooperative spectrum sensing method based on connected-dominating-set is proposed. In this paper, the connected-dominating-set method collects information of network spectrum sensing and does consensus calculation through the network connected dominating subset, which gets a stable consensus result. Then, the consensus results are shared to other non-dominated nodes in the network, so rapid convergence is achieved. There are two advantages of the proposed method compared with the original network distributed cooperative consensus method. One is that the amount of information exchanged among network nodes reduces, the other is that the network can quickly converge to a stable and accurate consensus result. The proof of convergence theorem of distribution consensus is given in this paper. Lastly, the simulation results show the excellent characteristics of this new algorithm.
Aiming at the problems of large amount of information exchange, slow convergence and unstable convergence result of the original whole-network distributed cooperative consensus method, a distributed consensus cooperative spectrum sensing method based on connected-dominating-set is proposed. In this paper, the connected-dominating-set method collects information of network spectrum sensing and does consensus calculation through the network connected dominating subset, which gets a stable consensus result. Then, the consensus results are shared to other non-dominated nodes in the network, so rapid convergence is achieved. There are two advantages of the proposed method compared with the original network distributed cooperative consensus method. One is that the amount of information exchanged among network nodes reduces, the other is that the network can quickly converge to a stable and accurate consensus result. The proof of convergence theorem of distribution consensus is given in this paper. Lastly, the simulation results show the excellent characteristics of this new algorithm.
2017, 46(5): 666-672.
doi: 10.3969/j.issn.1001-0548.2017.05.005
Abstract:
An innovative EE-oriented 'win-win' cooperative transmission scheme in heterogeneous cognitive radio networks (Het-CRNs), called energy-efficient cognitive cooperation (ECC), in which primary system release a part of its radio spectrum to secondary system in exchange for secondary relay (SR) served as a relay to assist transmission of primary user's traffic under the dual quality of service (QoS) requirements. Then, secondary user's traffic flow is split into the unlicensed spectrum and the released licensed spectrum to improve EE in Het-CRNs. Based on ECC, we formulate a weighted EE maximization non-convex problem for secondary users. By employing parametric fractional programming and golden section search (GSS) method, an efficient resource allocation policy is developed. Simulation results show that the proposed strategy can gain significantly higher EE without reducing performance of primary transmission, and thus, a 'win-win' goal is achieved.
An innovative EE-oriented 'win-win' cooperative transmission scheme in heterogeneous cognitive radio networks (Het-CRNs), called energy-efficient cognitive cooperation (ECC), in which primary system release a part of its radio spectrum to secondary system in exchange for secondary relay (SR) served as a relay to assist transmission of primary user's traffic under the dual quality of service (QoS) requirements. Then, secondary user's traffic flow is split into the unlicensed spectrum and the released licensed spectrum to improve EE in Het-CRNs. Based on ECC, we formulate a weighted EE maximization non-convex problem for secondary users. By employing parametric fractional programming and golden section search (GSS) method, an efficient resource allocation policy is developed. Simulation results show that the proposed strategy can gain significantly higher EE without reducing performance of primary transmission, and thus, a 'win-win' goal is achieved.
2017, 46(5): 673-678.
doi: 10.3969/j.issn.1001-0548.2017.05.006
Abstract:
In cooperative networks with automatic repeat request (ARQ), the multiple-rounds retransmission is used to improve the reliability at the cost of additional delay. In this paper, a cross-layer cooperative strategy is investigated in ARQ cooperative networks for the case that relay nodes do not have ability of signal combining reception. A selection cooperation scheme is proposed, in which both the decoding set and the selection of best relay are changed dynamically during each round of retransmission. In Rayleigh fading channels, the proposed scheme can achieve optimal diversity-multiplexing-delay (D-M-D) tradeoff without increasing the receiver complexity of relay nodes. Simulation results show that the outage probability performance of the proposed scheme is close to that of the scheme using combining reception at relay nodes. It achieves better tradeoff between the receiver complexity and the system performance.
In cooperative networks with automatic repeat request (ARQ), the multiple-rounds retransmission is used to improve the reliability at the cost of additional delay. In this paper, a cross-layer cooperative strategy is investigated in ARQ cooperative networks for the case that relay nodes do not have ability of signal combining reception. A selection cooperation scheme is proposed, in which both the decoding set and the selection of best relay are changed dynamically during each round of retransmission. In Rayleigh fading channels, the proposed scheme can achieve optimal diversity-multiplexing-delay (D-M-D) tradeoff without increasing the receiver complexity of relay nodes. Simulation results show that the outage probability performance of the proposed scheme is close to that of the scheme using combining reception at relay nodes. It achieves better tradeoff between the receiver complexity and the system performance.
2017, 46(5): 679-684, 794.
doi: 10.3969/j.issn.1001-0548.2017.05.007
Abstract:
To eliminate interference and improve the transmission rate of cognitive radio multiple-input multiple-output (CR-MIMO) systems, an interference alignment algorithm based on game theory is proposed. The algorithm uses water-filling algorithm for maximum the transmission rate of primary user. Meanwhile, the pre-coding matrix of secondary users is designed for the secondary user signal to fall into free sub-channel of the primary user. Multiple interference links are constituted into a game group to achieve interference alignment of secondary users. Moreover, the power allocation of secondary users is formulated as selection problem of cuckoo's nests, the optimal power allocation is obtained according to the fitness function. Numerical simulation results show that this algorithm can eliminate the interference between the primary user and secondary users and the interference among secondary users. Compared with the maximize-signal-to-interference-plus-noise-ratio algorithm (Max-SINR), the interference alignment algorithm proposed can improve the transmission rate of secondary users about 2 b·s-1·Hz-2. Moreover, the transmission rate can also be improved by using cuckoo search algorithm for power distribution compared with the result presented elsewhere.
To eliminate interference and improve the transmission rate of cognitive radio multiple-input multiple-output (CR-MIMO) systems, an interference alignment algorithm based on game theory is proposed. The algorithm uses water-filling algorithm for maximum the transmission rate of primary user. Meanwhile, the pre-coding matrix of secondary users is designed for the secondary user signal to fall into free sub-channel of the primary user. Multiple interference links are constituted into a game group to achieve interference alignment of secondary users. Moreover, the power allocation of secondary users is formulated as selection problem of cuckoo's nests, the optimal power allocation is obtained according to the fitness function. Numerical simulation results show that this algorithm can eliminate the interference between the primary user and secondary users and the interference among secondary users. Compared with the maximize-signal-to-interference-plus-noise-ratio algorithm (Max-SINR), the interference alignment algorithm proposed can improve the transmission rate of secondary users about 2 b·s-1·Hz-2. Moreover, the transmission rate can also be improved by using cuckoo search algorithm for power distribution compared with the result presented elsewhere.
2017, 46(5): 685-691, 794.
doi: 10.3969/j.issn.1001-0548.2017.05.008
Abstract:
Sparse code multiple access (SCMA) and low density parity check (LDPC) are the promising candidate multiple access and channel code technology for the future mobile communication systems. In order to enhance the multi-user detection performance of SCMA, a factor graph based joint detection and decoding (JDD) for SCMA and LDPC is proposed in this paper. In the proposed scheme, the multi-user detection of SCMA is aided by the extrinsic information of LDPC decoding. Simulation results show that the JDD scheme has obvious performance gains compared with traditional methods, and approximates to the performance of a single user transmission system. In order to trade off the performance and complexity, a reduced complexity joint detection and decoding (RC-JDD) scheme based on Jacobi logarithm approximation and message damping is proposed. The simulation and analysis results show that the JDD scheme has a much lower complexity with an acceptable performance.
Sparse code multiple access (SCMA) and low density parity check (LDPC) are the promising candidate multiple access and channel code technology for the future mobile communication systems. In order to enhance the multi-user detection performance of SCMA, a factor graph based joint detection and decoding (JDD) for SCMA and LDPC is proposed in this paper. In the proposed scheme, the multi-user detection of SCMA is aided by the extrinsic information of LDPC decoding. Simulation results show that the JDD scheme has obvious performance gains compared with traditional methods, and approximates to the performance of a single user transmission system. In order to trade off the performance and complexity, a reduced complexity joint detection and decoding (RC-JDD) scheme based on Jacobi logarithm approximation and message damping is proposed. The simulation and analysis results show that the JDD scheme has a much lower complexity with an acceptable performance.
2017, 46(5): 692-696.
doi: 10.3969/j.issn.1001-0548.2017.05.009
Abstract:
A hybrid method that combines the theory of superstition of active element pattern (AEP) and invasive weed optimization (IWO) is proposed for calibrating the beam direction of small antenna arrays. For a small antenna array with only a few elements, the beam direction discrepancy of each element will arise due to the mutual coupling of antenna elements if the array is fed with phase distribution based on the array factor (AF) theory. In this paper, the mutual coupling effects among elements in a small antenna array are considered. AEP method is employed to synthesize the radiation pattern of the antenna array. The phase distribution is optimized by the IWO. With the proposed hybrid method, the beam peak of the small antenna array can accurately steer to the desired direction.
A hybrid method that combines the theory of superstition of active element pattern (AEP) and invasive weed optimization (IWO) is proposed for calibrating the beam direction of small antenna arrays. For a small antenna array with only a few elements, the beam direction discrepancy of each element will arise due to the mutual coupling of antenna elements if the array is fed with phase distribution based on the array factor (AF) theory. In this paper, the mutual coupling effects among elements in a small antenna array are considered. AEP method is employed to synthesize the radiation pattern of the antenna array. The phase distribution is optimized by the IWO. With the proposed hybrid method, the beam peak of the small antenna array can accurately steer to the desired direction.
2017, 46(5): 697-702.
doi: 10.3969/j.issn.1001-0548.2017.05.010
Abstract:
A new algorithm of real-time detection for infrared motion small targets in rotational and complex background is proposed for solving the problems of high error rate of detection and poor real-time performance. The algorithm, at the first, processes the original infrared image with median filter, calculates the optical flows field, extracts the image's feature points, estimates the background optical flows field, and then extracts the assemblage of the target feature points by setting the threshold. Finally, according to the optical flow vector angle of feature points, target gray interval and the area of feature points of edge detection, the background features points are removed from the assemblage, and thus the infrared motion small targets in rotational and complex background are detected accurately and timely. The experimental results show that the rate of detection of infrared motion small targets reaches 93.8%, the rate of average false alarm is 0.126 times per frame, the average time of target detection per frame is 15.53 milliseconds, and the maximum processing time for each frame is 20.45 milliseconds. It is concluded that the proposed algorithm meets the requirements of real-time moving target detection.
A new algorithm of real-time detection for infrared motion small targets in rotational and complex background is proposed for solving the problems of high error rate of detection and poor real-time performance. The algorithm, at the first, processes the original infrared image with median filter, calculates the optical flows field, extracts the image's feature points, estimates the background optical flows field, and then extracts the assemblage of the target feature points by setting the threshold. Finally, according to the optical flow vector angle of feature points, target gray interval and the area of feature points of edge detection, the background features points are removed from the assemblage, and thus the infrared motion small targets in rotational and complex background are detected accurately and timely. The experimental results show that the rate of detection of infrared motion small targets reaches 93.8%, the rate of average false alarm is 0.126 times per frame, the average time of target detection per frame is 15.53 milliseconds, and the maximum processing time for each frame is 20.45 milliseconds. It is concluded that the proposed algorithm meets the requirements of real-time moving target detection.
2017, 46(5): 703-708.
doi: 10.3969/j.issn.1001-0548.2017.05.011
Abstract:
As a novel technique, compressed sensing, which can reduce energy consumption can promote the development of remote health monitoring systems based on wearable device. Dictionary learning algorithm has attracted much attention because of its improvement of the performance of reconstructing physiological signals in the field of compressed sensing. Usually, conventional dictionary learning algorithms did not consider the implicit correlation inside signals, resulting in that the characteristic of signals cannot be efficiently captured and thus the signal cannot be accurately reconstructed. In this paper, a correlation based dictionary learning algorithm is proposed to apply in compressed sensing, exploit implicit correlation structure inside the physiological signal efficiently, and overcome the shortcoming, poor reconstruction accuracy, of conventional dictionary learning algorithms. Experiments results show that the proposed algorithm can capture the structure of physiological signal adequately, and thus can improve the signal-to-noise ratio for compressed sensing, namely, the compressed physiological signal can be accurately reconstructed.
As a novel technique, compressed sensing, which can reduce energy consumption can promote the development of remote health monitoring systems based on wearable device. Dictionary learning algorithm has attracted much attention because of its improvement of the performance of reconstructing physiological signals in the field of compressed sensing. Usually, conventional dictionary learning algorithms did not consider the implicit correlation inside signals, resulting in that the characteristic of signals cannot be efficiently captured and thus the signal cannot be accurately reconstructed. In this paper, a correlation based dictionary learning algorithm is proposed to apply in compressed sensing, exploit implicit correlation structure inside the physiological signal efficiently, and overcome the shortcoming, poor reconstruction accuracy, of conventional dictionary learning algorithms. Experiments results show that the proposed algorithm can capture the structure of physiological signal adequately, and thus can improve the signal-to-noise ratio for compressed sensing, namely, the compressed physiological signal can be accurately reconstructed.
2017, 46(5): 709-715.
doi: 10.3969/j.issn.1001-0548.2017.05.012
Abstract:
The cloud security evolved from cloud computing has become a research hotspot in network security field, and the survivability of cloud security is particularly important. The current research status of survivability is analyzed firstly. Then the definition of cognitive survivability based on cognitive computing is proposed, and the ability of cognitive survivability is discussed. Finally, the cognitive survival model based on semi-Markov performance evaluation process algebra (SM-PEPA) is formalized and the solution of the quantitative assessment is described. The simulation experiment uses the cognitive survival index as evaluation parameters, such as survival threats, the ability to resist attacks and the richness of strategies. The proposed model is validated by solving the first passage time probability density function.
The cloud security evolved from cloud computing has become a research hotspot in network security field, and the survivability of cloud security is particularly important. The current research status of survivability is analyzed firstly. Then the definition of cognitive survivability based on cognitive computing is proposed, and the ability of cognitive survivability is discussed. Finally, the cognitive survival model based on semi-Markov performance evaluation process algebra (SM-PEPA) is formalized and the solution of the quantitative assessment is described. The simulation experiment uses the cognitive survival index as evaluation parameters, such as survival threats, the ability to resist attacks and the richness of strategies. The proposed model is validated by solving the first passage time probability density function.
2017, 46(5): 716-721.
doi: 10.3969/j.issn.1001-0548.2017.05.013
Abstract:
For cryptography protocols, security is its most core issue, and is the same for quantum cryptography protocols. Researchers can adopt many methods to prove that these protocols are secure, but there exists much difficulty. By using the method of formal verification and the technique of model checking, a fully automated probabilistic model checking tool-PRISM can be used to model these protocols and verify the security properties. Such a methodology can not only avoid the computational complexity of the traditional verification methods based on mathematics, but also improve efficiency and accelerate the verification process. The verification results show that the detection rate of eavesdropping is approximately close to 1 when sufficient photons are transmitted. The semi-quantum cryptography protocols is as secure as the full quantum protocols.
For cryptography protocols, security is its most core issue, and is the same for quantum cryptography protocols. Researchers can adopt many methods to prove that these protocols are secure, but there exists much difficulty. By using the method of formal verification and the technique of model checking, a fully automated probabilistic model checking tool-PRISM can be used to model these protocols and verify the security properties. Such a methodology can not only avoid the computational complexity of the traditional verification methods based on mathematics, but also improve efficiency and accelerate the verification process. The verification results show that the detection rate of eavesdropping is approximately close to 1 when sufficient photons are transmitted. The semi-quantum cryptography protocols is as secure as the full quantum protocols.
2017, 46(5): 722-727.
doi: 10.3969/j.issn.1001-0548.2017.05.014
Abstract:
For the data privacy in wireless body area network (WBAN), a secure privacy preserving k-nearest neighbor (kNN) query protocol for WBAN is proposed. This protocol can protect data privacy and access control by encrypting both data and queries with asymmetric scalar-product-preserving encryption (ASPE). To improving searching efficiency, we combine the technologies of R-tree and bucket partition and propose a data structure, named BRtree, for indexing data items. BRtree can significantly eliminate the unnecessary searching branches. In order to achieve access control, we separate an access key from the encryption key and introduce a trusted third authority to manage access rights and access rights transferring. The experimental results validate the efficiency of our scheme.
For the data privacy in wireless body area network (WBAN), a secure privacy preserving k-nearest neighbor (kNN) query protocol for WBAN is proposed. This protocol can protect data privacy and access control by encrypting both data and queries with asymmetric scalar-product-preserving encryption (ASPE). To improving searching efficiency, we combine the technologies of R-tree and bucket partition and propose a data structure, named BRtree, for indexing data items. BRtree can significantly eliminate the unnecessary searching branches. In order to achieve access control, we separate an access key from the encryption key and introduce a trusted third authority to manage access rights and access rights transferring. The experimental results validate the efficiency of our scheme.
2017, 46(5): 728-733, 746.
doi: 10.3969/j.issn.1001-0548.2017.05.015
Abstract:
Multi-channel multi-radio (MCMR) has been widely used in wireless mesh networks for improving the network performance. Two primary problems are faced in existing channel assignment algorithms. One is that it is impossible to achieve global optimization because both the time and space complexity are high. The other problem is that those algorithms can not be scaled flexibly and, thus, cannot be applied to large networks. To solve the above problems, this paper models the channel assignment problem with particle swarm optimization model by utilizing its advantages of fast convergence and low cost. Based on the network message exchange and interference model, a particle swarm optimization based channel assignment algorithm (PSOCA) is proposed. This algorithm aims at minimizing the fitness function with constraints of radios, channels, interference and so on. Through intensive simulations, the algorithm proposed is proved feasible, both the network throughput and packet drop ratio are remarkably improved in comparison with other similar algorithms.
Multi-channel multi-radio (MCMR) has been widely used in wireless mesh networks for improving the network performance. Two primary problems are faced in existing channel assignment algorithms. One is that it is impossible to achieve global optimization because both the time and space complexity are high. The other problem is that those algorithms can not be scaled flexibly and, thus, cannot be applied to large networks. To solve the above problems, this paper models the channel assignment problem with particle swarm optimization model by utilizing its advantages of fast convergence and low cost. Based on the network message exchange and interference model, a particle swarm optimization based channel assignment algorithm (PSOCA) is proposed. This algorithm aims at minimizing the fitness function with constraints of radios, channels, interference and so on. Through intensive simulations, the algorithm proposed is proved feasible, both the network throughput and packet drop ratio are remarkably improved in comparison with other similar algorithms.
2017, 46(5): 734-740.
doi: 10.3969/j.issn.1001-0548.2017.05.016
Abstract:
To provide quick name lookup technique, the paper designs a Hash bloom filter (HBF). The HBF consists of g on-chip counter bloom filters (CBFs), g on-chip counters and g off-chip Hash tables. Each Hash table is associated with a CBF and a counter. To reduce the false positive rate introduced by unbalanced name insertion in to CBFs, we propose two-Hash-choice algorithm which evenly disperses the FIB/CS/PIT entries into g Hash tables and CBFs. Moreover, HBF has a good feature of parallel processing of data packet forwarding because HBF adopts multiple Hash tables and CBFs. Theoretical and simulated results demonstrate that HBF can achieve very efficient name lookup by well utilizing the on-chip memory through localization and filtering function of CBF. Therefore, the proposed HBF improves data packet forwarding rate and effectively avoids flooding attacks.
To provide quick name lookup technique, the paper designs a Hash bloom filter (HBF). The HBF consists of g on-chip counter bloom filters (CBFs), g on-chip counters and g off-chip Hash tables. Each Hash table is associated with a CBF and a counter. To reduce the false positive rate introduced by unbalanced name insertion in to CBFs, we propose two-Hash-choice algorithm which evenly disperses the FIB/CS/PIT entries into g Hash tables and CBFs. Moreover, HBF has a good feature of parallel processing of data packet forwarding because HBF adopts multiple Hash tables and CBFs. Theoretical and simulated results demonstrate that HBF can achieve very efficient name lookup by well utilizing the on-chip memory through localization and filtering function of CBF. Therefore, the proposed HBF improves data packet forwarding rate and effectively avoids flooding attacks.
2017, 46(5): 741-746.
doi: 10.3969/j.issn.1001-0548.2017.05.017
Abstract:
The problem of replica placement is a key issue of distributed storage systems in cloud computing. Aiming at the uneven load among nodes and the high costs of replicas access, we propose a novel replica placement algorithm based on the immune optimization strategy. Through the computation of affinity of nodes, and with the help of clonal selection and immune memory mechanisms, the proposed algorithm can comprehensively evaluate and select the nodes for replica placement. Several simulations and conducted based on Matlab, and the results show that the algorithmpresented is able to reduce the cost of replica access, and makes the load between nodes more balanced.
The problem of replica placement is a key issue of distributed storage systems in cloud computing. Aiming at the uneven load among nodes and the high costs of replicas access, we propose a novel replica placement algorithm based on the immune optimization strategy. Through the computation of affinity of nodes, and with the help of clonal selection and immune memory mechanisms, the proposed algorithm can comprehensively evaluate and select the nodes for replica placement. Several simulations and conducted based on Matlab, and the results show that the algorithmpresented is able to reduce the cost of replica access, and makes the load between nodes more balanced.
2017, 46(5): 747-754.
doi: 10.3969/j.issn.1001-0548.2017.05.018
Abstract:
To address the problem that the teaching arrangements are not on the basis of recommendation in current school, a series of formalized methods are used to specify teachers' specialty foundation, course difficulty, and teaching evaluation first. Then, a kind of weighted function is defined to calculate the comprehensive partial weight for each group of teachers' professional foundation, course difficulty, and teaching evaluation. Next, the three-dimensional tensor model with partial weight is built on the 4-tuples relation of teacher-courseevaluation-weight and the comprehensive weight is endowed to the tensor elements. Finally, on the basis of above, a new kind of decomposition algorithm based on Tucker Decomposition is designed to obtain the approximate tensor of dimensionality reduction with the higher-order singular value decomposition (HOSVD), achieving the Top-N recommendation of teaching arrangements. Experiment results show that our proposed method can realize precise teaching arrangements recommendations when the iterative threshold value reaches a reasonable value, which can be used as a new intelligent recommendation method applied to the teaching arrangements in all kinds of schools.
To address the problem that the teaching arrangements are not on the basis of recommendation in current school, a series of formalized methods are used to specify teachers' specialty foundation, course difficulty, and teaching evaluation first. Then, a kind of weighted function is defined to calculate the comprehensive partial weight for each group of teachers' professional foundation, course difficulty, and teaching evaluation. Next, the three-dimensional tensor model with partial weight is built on the 4-tuples relation of teacher-courseevaluation-weight and the comprehensive weight is endowed to the tensor elements. Finally, on the basis of above, a new kind of decomposition algorithm based on Tucker Decomposition is designed to obtain the approximate tensor of dimensionality reduction with the higher-order singular value decomposition (HOSVD), achieving the Top-N recommendation of teaching arrangements. Experiment results show that our proposed method can realize precise teaching arrangements recommendations when the iterative threshold value reaches a reasonable value, which can be used as a new intelligent recommendation method applied to the teaching arrangements in all kinds of schools.
2017, 46(5): 755-765.
doi: 10.3969/j.issn.1001-0548.2017.05.019
Abstract:
The identification of disease genes is the crucial step in uncovering disease pathology and systematically analyzing polygenetic disease. The high-throughput technology has advanced the development of network-based approaches for disease gene prediction. Based on the "guilt-by-association" principle, now disease gene prioritization methods can measure the proximity between candidate genes and causal genes so as to pinpoint the potential disease genes. In this review, we first classify the network-based approaches for disease gene prediction into three categories:the approach based on disease genes information, the approach integrated with phenotype similarity and the approach that integrates several results from multiple data resources into one final result. Then we bring out the current situation of these approaches and summarize the current achievements and existing problems. Finally we put forward some suggestions for future research.
The identification of disease genes is the crucial step in uncovering disease pathology and systematically analyzing polygenetic disease. The high-throughput technology has advanced the development of network-based approaches for disease gene prediction. Based on the "guilt-by-association" principle, now disease gene prioritization methods can measure the proximity between candidate genes and causal genes so as to pinpoint the potential disease genes. In this review, we first classify the network-based approaches for disease gene prediction into three categories:the approach based on disease genes information, the approach integrated with phenotype similarity and the approach that integrates several results from multiple data resources into one final result. Then we bring out the current situation of these approaches and summarize the current achievements and existing problems. Finally we put forward some suggestions for future research.
2017, 46(5): 766-776.
doi: 10.3969/j.issn.1001-0548.2017.05.020
Abstract:
Identifying influential nodes is a hotspot issue in the research area of network science, and is of great significance in both theory and application. In recent studies, the H-index which was used to estimate the scientific impact of scientists was applied to identify influential nodes on complex networks. A fundamental relation, called the DHC theorem, was found among node degree, H-index, and coreness. In this paper, we extend the DHC theorem to directed and weighted networks, and proof that the DHC theorem is also valid. Then on this basis, this paper compares the ranking accuracy and resolution of the centralities in real weighted networks, and explores the role of weight ranking accuracy. Finally, by using the directed and weighted DHC theorem, the paper deeply analyzes the China Microblog retweet network between cities, assesses the online media influence of Chinese cities, and summarizes the information propagation patterns between users in different cities.
Identifying influential nodes is a hotspot issue in the research area of network science, and is of great significance in both theory and application. In recent studies, the H-index which was used to estimate the scientific impact of scientists was applied to identify influential nodes on complex networks. A fundamental relation, called the DHC theorem, was found among node degree, H-index, and coreness. In this paper, we extend the DHC theorem to directed and weighted networks, and proof that the DHC theorem is also valid. Then on this basis, this paper compares the ranking accuracy and resolution of the centralities in real weighted networks, and explores the role of weight ranking accuracy. Finally, by using the directed and weighted DHC theorem, the paper deeply analyzes the China Microblog retweet network between cities, assesses the online media influence of Chinese cities, and summarizes the information propagation patterns between users in different cities.
2017, 46(5): 777-783.
doi: 10.3969/j.issn.1001-0548.2017.05.021
Abstract:
Most of the existing methods of extracting keyword based on complex networks ignore the natural language characters when building the weighted text network. In the meantime, they involve less the classical algorithms in complex network field. Based on PageRank algorithm, we propose a keyword extraction method, named LTWPR (located and TF-weighted PageRank), which takes into consideration term-frequency character and human language characters. The algorithm creates a term-frequency-shared weight in order to share the node's term-frequency value to its links, and defines a position weight coefficient to express different importance of words in different positions of news articles. LTWPR brings text networks' local and global features into consideration, making the results more accurate. Comprehensive experiments are conducted based on news articles grabbed from Sina News. Experimental results show that LTWPR algorithm is more effective and can better cover the keywords tagged by authors.
Most of the existing methods of extracting keyword based on complex networks ignore the natural language characters when building the weighted text network. In the meantime, they involve less the classical algorithms in complex network field. Based on PageRank algorithm, we propose a keyword extraction method, named LTWPR (located and TF-weighted PageRank), which takes into consideration term-frequency character and human language characters. The algorithm creates a term-frequency-shared weight in order to share the node's term-frequency value to its links, and defines a position weight coefficient to express different importance of words in different positions of news articles. LTWPR brings text networks' local and global features into consideration, making the results more accurate. Comprehensive experiments are conducted based on news articles grabbed from Sina News. Experimental results show that LTWPR algorithm is more effective and can better cover the keywords tagged by authors.
2017, 46(5): 784-789.
doi: 10.3969/j.issn.1001-0548.2017.05.022
Abstract:
The differential pulse stripping voltammetry is used to detect the lead and cadmium heavy metal ions in liquid phase. The experimental parameters can be gradually optimized by changing the experimental conditions. The experimental results are analyzed and discussed. The detection limits for lead and cadmium ions are 0.54 g/L, 0.79 g/L, respectively, and the linear correlation coefficient of cadmium reaches to 0.999 7. The voltammetry used here has merits such as high sensitivity, easy operation, simple structure, low pollution, repeatable measurement, and show potential application for real-time monitoring of heavy metal ions.
The differential pulse stripping voltammetry is used to detect the lead and cadmium heavy metal ions in liquid phase. The experimental parameters can be gradually optimized by changing the experimental conditions. The experimental results are analyzed and discussed. The detection limits for lead and cadmium ions are 0.54 g/L, 0.79 g/L, respectively, and the linear correlation coefficient of cadmium reaches to 0.999 7. The voltammetry used here has merits such as high sensitivity, easy operation, simple structure, low pollution, repeatable measurement, and show potential application for real-time monitoring of heavy metal ions.
2017, 46(5): 790-794.
doi: 10.3969/j.issn.1001-0548.2017.05.023
Abstract:
A self-powered pedometer based on triboelectric nanogenerator is designed and presented. The sensor is composed of accessible and low-cost materials such as Al, Cu, polydimethylsiloxane (PDMS) and Kapton. The output voltage of the sensor can reach 11 V and 40 V when the subject with the sensor goes for a walk or run, respectively. Finite element methods is used to calculate the potential distribution of the sensor via COMSOL, and the simulation results correspond well with the experiment results. The electrical signal has good stability and repeatability. The pace frequency distribution can be precisely obtain via the frequency-domain analysis of signal character. This sensor has the potential applications in the next generation pedometer with low power consumption or even self-powered ability. This will play a unique role in the fields of health care and smart wearable electronics.
A self-powered pedometer based on triboelectric nanogenerator is designed and presented. The sensor is composed of accessible and low-cost materials such as Al, Cu, polydimethylsiloxane (PDMS) and Kapton. The output voltage of the sensor can reach 11 V and 40 V when the subject with the sensor goes for a walk or run, respectively. Finite element methods is used to calculate the potential distribution of the sensor via COMSOL, and the simulation results correspond well with the experiment results. The electrical signal has good stability and repeatability. The pace frequency distribution can be precisely obtain via the frequency-domain analysis of signal character. This sensor has the potential applications in the next generation pedometer with low power consumption or even self-powered ability. This will play a unique role in the fields of health care and smart wearable electronics.
2017, 46(5): 795-799.
doi: 10.3969/j.issn.1001-0548.2017.05.024
Abstract:
The micro-fluxgate sensor is widely used in the field of weak magnetic field measurement such as geophysical exploration, geomagnetic navigation, space environment monitoring, medical diagnosis and treatment and others because of its characteristics of low power consumption, high sensitivity, high resolution, simple structure and low cost. The excitation module directly determines the performance of the whole system and thus it is of significance to build an accuracy simulation model of the module. The mathematical model of the dual cores is built on the basis of the Faraday's law of electromagnetic induction, and the voltage waveforms and the magnetic value under the sinusoidal excitation are given. According to the structure parameters of the KDM-01 which is a fluxgate sensor designed by Institute of Geology and Geophysics, Chinese Academy of Sciences, a simulation model of the excitation module is built. The obtained simulation results are in good accordance to the test values. Then the model is applied to the designed magnetometer and the test data are similar enough to those of the standard magnetometer. It is proved that the built mathematical model for the excitation module is reasonable and accurate.
The micro-fluxgate sensor is widely used in the field of weak magnetic field measurement such as geophysical exploration, geomagnetic navigation, space environment monitoring, medical diagnosis and treatment and others because of its characteristics of low power consumption, high sensitivity, high resolution, simple structure and low cost. The excitation module directly determines the performance of the whole system and thus it is of significance to build an accuracy simulation model of the module. The mathematical model of the dual cores is built on the basis of the Faraday's law of electromagnetic induction, and the voltage waveforms and the magnetic value under the sinusoidal excitation are given. According to the structure parameters of the KDM-01 which is a fluxgate sensor designed by Institute of Geology and Geophysics, Chinese Academy of Sciences, a simulation model of the excitation module is built. The obtained simulation results are in good accordance to the test values. Then the model is applied to the designed magnetometer and the test data are similar enough to those of the standard magnetometer. It is proved that the built mathematical model for the excitation module is reasonable and accurate.
