Abstract: The mobile agent is a computer program that is able to migrate continuously among hosts in a network and use host service to meet its task. The host, known as workplace, can be regarded as a proxy of social member. The sequence of workplaces on which the mobile agent completed its tasks is called path. In this paper, we propose a dynamic building method of mobile agent path with minimum payment based on referral. By referral, the next workplace of mobile agent can be recommended by the current workplace provider based on his acquaintance knowledge. The simulation results on a random network model show that the more acquaintance relationships there are on the referral network, the more efficiently the mobile agent path can be built, and the fewer costs need to be paid on the path.
Abstract: Petri nets are widely used in describing workflow models of management systems, and also functioning in process management and resource organizing. The number of workflow patterns are increasing along with the demands of business process modeling. The Petri net and its extensions can no longer meet the requirement of model description, due to the limitation of their semantics. The models of some patterns are very sophisticated and even grow exponentially in complexity. Also the performance of these new patterns are not well studied. This paper extends Petri nets to Stochastic workflow nets (SWNs) and introduces stochastic or state related variables into arc weights, transition enabling guard and execution time. With the new features, SWNs can describe models more accurately and flexibly. And performance of common patterns is calculated under the assumption of exponentially distributed time. The paper also illustrates how to simplify the model with equivalent patterns.
Abstract: Verifiable secret sharing (VSS) is an important technique which has been used as a basic tool in distributed cryptosystems, secure multi-party computations, as well as safe guarding some confidential information such as cryptographic keys. By now, some secure and efficient non-interactive VSS schemes for sharing secrets in a finite field have been available. In this paper, we investigate verifiably sharing of a secret that is an element of a bilinear group. We present an efficient and informationtheoretical secure VSS scheme for sharing such a secret which may be a private key for a pairing based cryptosystem. Our performance and security analysis indicates that the newly proposed scheme is more efficient and practical while enjoys the same level of security compared with similar protocols available. We also demonstrate two typical applications of our proposed VSS scheme. One is the sharing of a secret key of Boneh and Franklin's identity-based encryption scheme, and the other is the sharing or the distributed generation of a secret key of the leakage resilient bilinear ElGamal encryption scheme.
Abstract: Taint analysis is a popular method in software analysis field including vulnerability/malware analysis. By identifying taint source and making suitable taint propagation rules, we could directly know whether variables in software have any relationship with input data. Static taint analysis method is efficient, but it is imprecise since runtime information is lacked. Dynamic taint analysis method usually instruments every instruction in software to catch the taint propagation process. However, this is inefficient since it usually takes lots of time for context switches between original code and instrumenting code. In this paper, we propose a statically-directed dynamic taint analysis method to increase the efficiency of taint analysis process without any loss of accuracy. In this way, there is no need to instrument every instruction. Several experiments are made on our prototype SDTaint and the results show that our method is several times more efficient than traditional dynamic taint analysis method.
Abstract: A new layered multi-robots architecture for cooperative online simultaneous localization and mapping based on Service-oriented architecture (SOA) is proposed. This architecture is constructed by the concepts of service-oriented and loosely coupled center scheduling. These methods make robots' underlying function realization be transparent in cooperation and avoid the impact affected by robot heterogeneous characteristics, which will be beneficial to the system construction, expansion, restructuring and maintenance. All data packages follow the SOA unified data format, which can avoid the impact affected by sensor heterogeneous characteristics. The point to point serving model reduces the path of data transmission which can improve the timeliness of the system. All of these features can satisfy the requirements of multi-robots cooperative online SLAM. The simulation results show the feasibility, applicability and practicability of this architecture.
Abstract: As one of the challenges for network virtualization, virtual network embedding which maps Virtual network (VN) to the substrate network and allocates resources according to the requirements of VN in an efficient way has gained great attention. Existing algorithms generally make their decision according to the present available substrate network resource, especially bandwidth. This paper proposes a time-based VN embedding algorithm. A probability model is formulated to obtain the maximum probability that the available resources of substrate network can be used by succeeding VN requests. The probability is set as the weight of the node and the greedy algorithm is employed to embed the virtual node. The reciprocal of the probability is set as the weight of the link and the shortest path algorithm is employed to embed the virtual link. Simulation experiments show that the proposed algorithm increases the acceptance rate and the revenue compared to the existing algorithms.
Abstract: A new clustering algorithm of Hierarchical grid clustering using data field (HGCUDF) is proposed. Under the distributed characteristics of data points on objects, the hierarchical grids divide and conquer the large datasets in their hierarchical subsets, which reduces the scope in search of the clustering centers, and minifies the area of data space for generating data field. The compared experiments show that HGCUDF computes the grids rather than retrieves all data from database, for improving the efficiency.
Abstract: Microkernel integrity is an important aspect of security for the whole microkernel system. Many of the research works on microkernel integrity focus on analysis and safeguards against the existing kernel attacks, and security enhancements for the vulnerabilities of system design and implementation aspects. The formal methods for operating system design and verification ensure the system's high level of security. The existing formalization work and research for operating system mainly focus on the code-level verification of program correctness. In this paper, we propose a formal abstraction model of microkernel in order to accurately describe the semantics of every kernel behavior, and achieve the description of the whole kernel. Based on the model we illustrate the microkernel integrity criterions and elaborate on the integrity mechanism. Meanwhile, we formally verify the completeness and consistency between the mechanism and the definition of the microkernel integrity. We use the self-implemented operating system VTOS (Verified trusted operating system) as an example to illustrate the design method for the microkernel integrity.
Abstract: Temporal difference (TD) learning family tries to learn a least-squares solution of an approximate Linear value function (LVF) to deal with large scale and/or continuous reinforcement learning problems. However, due to the represented ability of the features in LVF, the predictive error of the learned LVF is bounded by the residual between the optimal value function and the projected optimal value function. In this paper, Temporal difference learning with Piecewise linear basis (PLB-TD) is proposed to further decrease the error bounds. In PLBTD, there are two steps: (1) build the piecewise linear basis for problems with different dimensions; (2) learn the parameters via some famous members from the TD learning family (linear TD, GTD, GTD2 or TDC), which complexity is O(n). The error bounds are proved to decrease to zero when the size of the piecewise basis goes into infinite. The empirical results demonstrate the effectiveness of the proposed algorithm.
Abstract: Arithmetic operations and expression evaluations are fundamental in computing models. This paper firstly designs arithmeticmembranes without priority rules for basic arithmetic operations, and then proposes an algorithm to construct expression P systems based on several of such membranes after designing synchronous and asynchronous transmission strategies among the membranes. For any arithmetic expression, an expression P system can be built to evaluate it effectively. Finally, we discuss different parallelism strategies through which different expression P systems can be built for an arithmetic expression.
Abstract: Efficiently mapping multiple independent Virtual networks (VNs) over a common infrastructure substrate is a challenging problem on cloud computing platforms and large-scale future Internet testbeds. Inspired by the idea of data fields, we apply a topological potential function to node ranking and propose an algorithm called Locality-aware node topological potential ranking (LNTPR), which can precisely and efficiently reflect the relative importance of nodes. Using LNTPR and the concept of locality awareness, we develop the Locality-aware influence choosing node (LICN) algorithm based on a node influence model that considers the mutual influence between a mapped node and its candidate mapping nodes. LNTPR and LICN improve the integration of node and link mapping. Simulation results demonstrate that the proposed algorithms exhibit good performance in determining revenue, acceptance ratio, and revenue/cost ratio.
Abstract: To satisfy the requirements of complex and special analog layout constraints, a constraint symbolization method based on geometric programming for analog layout retargeting is presented in this paper. Our approach is to build symbolic template for layouts, then uses Geometric programming (GP) to achieve new technology design rules, implement device symmetry and matching constraints, and manage parasitics optimization. The GP, a class of non-linear optimization problem, can be transferred or fitted into a convex optimization problem. Therefore, a global optimum solution can be achieved. The symbolizationmethod ensures the layout retargeting automatically. The efficiency and effectiveness of the proposed algorithm, as compared with the other existing methods, are demonstrated by a basic case-study example and a twostage Miller-compensated operational amplifier.
Abstract: In this paper, issues of speeding up Recurrent neural network language model (RNNLM) in the testing phase are explored so that RNNLMs can be used to re-rank a large n-best list in real-time systems which could obtain better performance. A new n-best list rescoring framework, Prefix tree based n-best list re-scoring (PTNR), is proposed to hundred percent eliminate the repeated computations which makes n-best list re-scoring ineffective. At the same time, the bunch mode technique, widely-used in speeding up the training of Feed-forward neural network language model (FF-NNLM), is combined with PTNR and the speed is further improved. Experimental results show that our approach is much faster than basic n-best list re-scoring. Take 1000-best as an example, our approach is almost 11 times faster than the basic n-best list re-scoring.
Abstract: Time-interleaved analog-to-digital converter (TIADC) is an efficient way to achieve higher sampling rate for medium-to-high resolution applications. The performance of a TIADC suffers from the mismatch errors among the sub-channels. This paper presents a method to estimate the channel mismatches using the sub-channels output data. The proposed method introduces an equivalent transfer function for each channel to model and estimate the mismatch errors. A Hybrid filter bank (HFB) structure is used to both model the TIADC and reconstruct the desired uniformly sampled sequence based on the perfect reconstruction conditions of the HFB system. A four-channel 12-bit 400MHz TIADC has been implemented in hardware to verify the proposed calibration method. The measured results show that the Spuriousfree dynamic range (SFDR) can be improved up to 74dB after being corrected with 64-tap Finite-impulse response (FIR) filters.
Abstract: Tissue P systems are a class of distributed and parallel computing models inspired from inter-cellular communication and cooperation between cells. In this work, a variant of tissue P system, named tissue P system with look-ahead mode, is discussed for decreasing the inherent non-determinism of tissue P systems and helping implementing tissue P systems on computers. Such systems are proved to be universal by simulating register machine, and they are also proved to be able to efficiently solve computationally hard problems by means of a spacetime tradeoff, which is illustrated with a polynomial solution to 3-coloring problem.
Abstract: Adaptive fuzzy spiking neural P systems (AFSN P systems) are a novel kind of computing models with parallel computing and learning ability. Based on our existing works, AFSN P systems are applied to deal with the fault diagnosis problems of power systems and the uncertainty of action messages about protective relays and breakers, and a new fault diagnosis model of power systems is proposed with simple reasoning process and fast speed with parallel processing capabilities. The effectiveness of the fault diagnosis model is verified by some examples of fault diagnosis. Furthermore, the learning ability of AFSN P systems can be applied to adjust the weights in the fault diagnosis model automatically.
Abstract: In this work, a distributed source positioning approach is developed based on Alternating direction method of multipliers (ADMM). First, a centralized positioning method is developed under case of the anchor uncertainty. And then, the method is realized in a distributed way using ADMM. Simulation results show that the centralized one is robust to the anchor errors and distributed one has similar performance as the centralized one.
Abstract: The Pulse coupled neural network (PCNN) has been widely used in digital image processing, but the automatic parameters determination is still a difficult aspect, which becomes the focus of PCNN research. In this paper, by the classical solution to difference equations and the time-domain analysis of PCNN model, we provide the expressions of the firing time and the firing period of neurons, and reveal the "mathematics firing" phenomenon of PCNN. Based on this, we propose a new method of automatic parameters determination based on both eliminating the "mathematics firing" and getting the highest efficiency of PCNN. We also present an edge detection model on the basis of image segmentation of PCNN and a method to determine automatically the parameters of the model. Experimental results prove the validity and efficiency of our proposed algorithm for the segmentation and the edge detection of the test images.
Abstract: In the discriminative sparse coding, the reconstruction residual over each class-specific subdictionary can provide great discriminative and label information. In this paper, we propose a weighted discriminative sparse coding method by using the residual as the weight. For a test sample, we first compute its sparse code over each learnt sub-dictionary, and then use the reconstruction residual over each sub-dictionary to weight the corresponding sub-dictionary, thereby forming a weighted sample-specific structure dictionary, over which we compute a new sparse code for the test sample. This code carries more discriminative information about interclass difference. Our method yields a unique structure dictionary for each test sample, so that samples with the same class labels have more similar distributions of dictionary atom contributions. Experimental results demonstrate that the proposed method outperforms some state-of-the-art methods under the same learning conditions.
Abstract: Part-Of-Speech tagging is a basic task in the field of natural language processing. This paper builds a POS tagger based on improved Hidden Markov model, by employing word clustering and syntactic parsing model. Firstly, In order to overcome the defects of the classical HMM, Markov family model (MFM), a new statistical model was introduced. Secondly, to solve the problem of data sparseness, we propose a bottom-to-up hierarchical word clustering algorithm. Then we combine syntactic parsing with part-of-speech tagging. The Part-of-;Speech tagging experiments show that the improved Part-Of-Speech tagging model has higher performance than Hidden Markov models (HMMs) under the same testing conditions, the precision is enhanced from 94.642% to 97.235%.
Abstract: Principal component analysis (PCA) combined with cluster analysis has become an effective approach for Near-infrared (NIR) chemical image analysis. Traditional cluster algorithms are sensitive to initial starting conditions and can be trapped into local optimal solutions. To overcome the drawbacks, we develop a new algorithm in this paper which improves Particle swarm optimization with Adaptive local optimization (ALO-PSO). Simulation experiments performed on NIR image of tablet verify the feasibility and effectiveness of the proposed algorithm. Experimental results of the clustering performances indicate that ALO-PSO algorithm offers an alternative approach for solving data clustering problems in NIR chemical image analysis.
Abstract: In order to provide users with intelligent retrieval services over large-scale semantic data, this paper proposes a MultikeyRank model. ORDPATHs is adopted to encode ontology classes defined in TBox and an inverted index is constructed according to the structured characteristics and semantic association of RDF data. A new query mode with one primary keyword and several auxiliary keywords is designed. To reflect the user's perference objectively, the membership degree for ontology classes corresponding to the primary keyword is calculated based on the evidence theory and thus the MultikeyRank algorithm is formulated by extending the BM25F model accordingly. The proposed model was implemented in the selfdeveloped distributed large-scale RDF data server "Jingwei" and experimental results show that compared with BM25F, the evaluation indexes for P@5, P@10, P@15 and MAP are improved by 27.6%, 24.3%, 18.5% and 3.7%, respectively.
Abstract: It is well known that the famous Constant modulus algorithm (CMA) presents a large steady-state Mean square error (MSE) for nonconstant modulus signals. In this paper a coordinate mapping approach for a 4-PAM nonconstant modulus signal is described which can change the signal to an Offset QPSK (OQPSK) constant modulus signal. And a new algorithm based on this approach is proposed which is also suitable for a 16-QAM signal. For the 4-PAM and 16-QAM nonconstant modulus signals the proposed algorithm can achieve a zero steadystate MSE in a noiseless environment but CMA cannot. Theoretical analysis and simulations results demonstrate the high performance of the proposed algorithm.
Abstract: Analysis of human activity and online anomaly detection from video sequences is one of the hottest and difficult research areas in computer visions. This paper describes a method for pedestrian gait classification in video sequence and deals with the classification of human gait types based on the notion that gait types can be analyzed into a series of consecutive postures types. First, silhouettes are extracted using the Background subtraction method which is combined with the time-stepping method. Then a method using recursion method for establishment of the standard gait state sequence is proposed. Meanwhile, wavelet moment method is used to extract features of the human body image, and the result matrix leads to Discrete hidden Markov models. Finally, Discrete hidden Markov models is used for human posture training, modeling and activity matching to recognize the human activity. The experiment tests show some encouraging results also indicates the algorithm has very small leak-examining and mistake-examining-rate, also shows the capability of realtime performance, which indicate that the method could be a choice for solving the problem but more tests are required.
Abstract: In this paper, a new supervised classification method, combining spectral and spatial information, is proposed. The method is based on the two following facts. First, a hyperspectral pixel can be sparsely represented by a linear combination of the dictionary consists of a few labeled samples. If any unknown hyperspectral pixel lies in the subspace spanned by some labeled-class samples, it will be classified to this labeled-class. And this is to solve a fully constrained sparse unmixing problem with the l2 regularization and the criterion of classification is relaxed to be determined by the largest value of sparse vector whose nonzero entries correspond to the weights of the labeled samples. Second, since the nearest neighbors probably belong to the same class, a spatial constraint is introduced. Alternating direction method of multipliers (ADMM) and the graph cut based method are then used to solve the spectral-spatial model. Finally, two real hyperspectral data sets are used to validate our proposed method. Experimental results show that the proposed method outperforms many of the state-of-the-art methods.
Abstract: Based on the certificateless public key cryptography and the trusted computing technologies, a certificateless based trusted access protocol for WLAN (Wireless local area networks) is proposed. Such protocol realizes the mutual authentication and unicast session key agreement between STA and AP within 3 protocol rounds. In particular, the platform authentication and integrity verification are achieved during the authentication procedure. The security properties of the new protocol are examined using the Extended Canetti-Krawczyk security model. The analytic comparisons show that the new protocol is very efficient in both computing and communications.
Abstract: We proposed a unified model for Chinese named entity recognition in micro-blogs. The models provide a simple statistical framework to incorporate a wide variety of linguistic knowledge and statistical models in a unified way. In our approach, KNN classifier is used to get suitable training data. An optimal algorithm to generate the hierarchically structured DSTCRF is executed to select the structure attributes of the named entity in micro-blogs knowledge. The experimental results showed that the accuracy rate was significantly improved.
Abstract: In this paper, we propose a method that builds power model template according to input transitions of combinatorial logic circuit. By computing its correlation with the overall power consumption of a cryptographic circuit, we are able to recover the secret key. Several simulation-based experiments have been conducted, which verifies the feasibility of our method and shows that the combinatorial logic is also faced with the problem of information leakage in power analysis cases. Compared with DPA (Differential power analysis) and CPA (Correlation power analysis), our attack is fairly effective against the cryptographic circuits whose protection is only implemented on the register parts of the sequential circuit. In addition, a few topics for further research, as well as the advices for more precise power model and countermeasures, are presented at the end of the paper.
Abstract: We investigate the entanglement dynamics of a quantum system consisting of three Superconducting charge qubits (SCQs) interacting with a single-mode cavity field. Considering the Rotating wave approximation (RWA) and the resonance condition, we give the effective Hamiltonian of the system involving cavity decay. The dynamical evolution is studied in terms of the entanglements in the different bipartite partitions of the system, as quantified by the square concurrence. With the choice of the appropriate initial states, a four-qubit W-entangled state can be obtained. We study the effect of cavity decay on various square concurrences in the system, the results show that the peak value of the square concurrence declines with the increase of cavity decay when the other parameters are fixed, but the cavity decay does not change the period of the square concurrence. We also find a natural entanglement transfer and entanglement invariant under evolution of the effective Hamiltonian.
Abstract: This paper considers a multi-antenna transmission strategy for high speed railway communications. In order to achieve better performance than conventional space-frequency block coding schemes, we propose a directional beamforming strategy for the High-speed railway (HSR) communication by exploiting some characteristic of the railway system including predetermined moving tracks and real-time positioning information. Moreover, for alleviating the effect of Doppler shift due to the moving train, a frequency offset precorrection method is also incorporated with direction beamforming. Theoretical Signalto-;noise ratio (SNR) gain of the proposed beamforming scheme over traditional HSR communication schemes is also derived for illustrating the performance enhancement. Numerical results verify the effectiveness of our proposed scheme even with some kind of imperfect position information available at the transmitter.
Abstract: In this paper, we depict in detail six subfamilies of implementation-friendly Barreto-Naehrig (BN) elliptic curves by choosing six special congruency classes of the curve-finding search parameter. These curves have small curve constants, support efficient tower extension options of finite field required in fast pairing implementation and have obvious generators for the bilinear cycle group G1. The detailed description will supply the implementor with more choices of suitable BN curves.
Abstract: Two knapsack public key cryptosystems based on randomized knapsack sequences were proposed in 2009 and 2011 respectively, where the secret knapsack sequence is transformed by secret modular linear transforms into one or three public randomized knapsack sequences with appropriate density. In this paper, we recover the secret modular linear transforms by simultaneous Diophantine approximation and propose secret key recovery attacks on these two cryptosystems. Practical attack experiments are done within one minute.
Abstract: In order to investigate the effect of network connectivity on the performance of wireless network coding, we introduce percolation theory to construct the system model of multi-hop wireless network for asymptotic connectivity. Concretely, we proposed a normalization algorithm for random network to layer the nodes of the largest connected component in multi-hop wireless network, and derived the theoretical conditions of percolation occurrence for the normalized hierarchical network of the largest connected component. Furthermore, according to the critical threshold of percolation phenomenon, we derived the performance of wireless network coding for the largest connected component. The mean delay and throughput were quantified in terms of network coding parameters such as coding window size, transmission radius, and node density. These conclusions clarify the effective performance of wireless network coding for random network, and will contribute to the evaluation of optimal performance of wireless network coding.
Abstract: Cloud storage can provide flexible and scalable data storage services to users. However, once data is uploaded to the cloud without a copy in local computers, the user loses control of the data physically. So, it is necessary to study a method to ensure users' data integrity. Avoiding retrieving enormous storage data or checking the data by users, a proof of storage protocol with public auditing was proposed based on the lattice cryptography. The user computed the signatures of the blocks, and outsourced them to cloud servers. Cloud service providers combined the blocks. Third party auditor verified all blocks' integrity only through the combined message and signature. Based on the Small integer solution assumption, the presented protocol is secure against the lost attack and tamper attack from cloud service providers. Based on the Learning with error assumption, the presented protocol is secure against the curiosity attack from third party auditor. The protocol is quite efficient, requiring just a few matrix-vector multiplications and samplings from discrete Gaussians.
Abstract: In city environment, the vehicle communication is affected by the around obstacles due to the especial condition of wireless channels. However, most of prior works adopt the fixed radio range value of vehicles to transmit packets. In this paper, we design an optimization forwarding range routing protocol for VANET in urban area. It has an optimized and adjustable forwarding range, which changes with different environments based on the path loss and the city model. And the proposed geo-routing protocol has a novel idea in computing the connectivity of roads and the adjustable strategy in a sparse network. Simulation results indicate that the OFRR enjoys desirable performance in the urban area.
Abstract: An Analytic estimation method (AEM) which has less execution time is proposed to calculate the User range accuracy (URA) used by the user to detect the integrity potential risk. It shows that the most important thing of computing URA is to find the maximum error vector in the error vector space. By using the covariance matrix of the error vector, the searching of the maximum error vector is converted to an analytic geometry problem. The value of the maximum error vector can be obtained directly by mathematical derivation. Experiments are made to compare the performance between the proposed AEM algorithm and the Exhaustive grid search (EGS) method. The related results show that the AEM algorithm can reduce more than 92% of execution time. This algorithm is more suitable for computing URA at the Master control station (MCS) for the engineering applications.
Abstract: A coupled-fed printed PIFA (Planar Inverted-F antenna) for eight-band operation covering the LTE700/2300/2500 and GSM850/900/1800/ 1900/UMTS2100 bands in the mobile phone is presented. The proposed antenna comprises a driven T-monopole and two coupled folded-strips. The folded strip which is coplaner with the T-monopole is called strip 1 and the folded-strip on the back of the system circuit board is called strip 2. The strip 1 is short-circuited to the system ground through an inductance and the Strip 2 is short-circuited to the system ground plane of the mobile phone directly. The driven T-monopole and the strip 1 contribute a wide band for the antenna's upper band (1710-2690MHz), whereas the strip 2 generates a resonant mode for the antenna's lower band (698-960MHz). The antenna's two wide operating bands are achieved and controlled by tuning the width of the T-monopole, the gap g3 between the strip 1 and the T-monopole, and the dimensions of the strip 2 on the back of the system circuit board. Details of the proposed antenna are described, and the obtained results are presented and discussed.
Abstract: A novel generalized simulation method is proposed to simulate the dynamic transmission delay of wideband and arbitrary signal in aerospace Tracking, telemetry and command (TT&C) channel. This method orthogonally demodulates the wideband and arbitrary Radio-frequency (RF) signal into complex baseband by a Local oscillator (LO) signal. Then the method of dynamic interpolation and delay reconstruction is proposed to obtain the delay reconstruction signal of complex baseband signal based on the variation rules of satellite-to-earth location. Meanwhile, the method of satellite-to-earth distance subsection and polynomial fitting is applied to obtain the delay reconstruction signal of LO signal. The simulated output signal is achieved through the synthesis of two delay reconstruction signals mentioned above. The proposed method can accurately simulate the variation characteristics of time delay and Doppler when wideband and arbitrary RF signal transmits in channel, without knowing any priori knowledge, such as signal form, signal parameters, and so on.
Abstract: On the basis of the simple ice-sheet freezethaw physical model, a new algorithm of Antarctic icesheet freeze-thaw detection was proposed for the automatic threshold segmentation, which did not depend on the priori freeze-thaw distribution. That was the histogram statistics for the data of ice-sheet freeze-thaw physical model by the use of generalized Gaussian model to automatically get the optimal threshold of the dry snow and the wet snow, so as to get the Antarctic freeze-thaw areas. The algorithm improves the computational efficiency, usability and operability of the ice-sheet freeze-thaw detection because the algorithm does not rely on the actual melt information and can automatically select many samples. To some extent, the algorithm also improves the accuracy of the ice-sheet freeze-thaw detection.
Abstract: Multifunction radar (MFR) is a sophisticated sensor which could perform several different functions simultaneously. The problems of deciphering the signals of MFR and inferring the information of its capabilities have posed a great challenge to the Electronic intelligence (ELINT) society. In this paper, we propose a method to reconstruct MFR's search plan from its signals by drawing lessons from the bioinformatics approach "Multiple sequence alignment (MSA)", which is used extensively for gene analysis. In the method, the signal of MFR is represented as a structured task sequence, and is segmented into several blocks according to the inherent periodicity of the search plan. Then, these blocks are aligned optimally to distinguish the tasks relevant to the search function from the others. Simulation results show that the proposed method is applicable and effective.