Abstract: Kernel is a key component of the Support vector machines (SVMs) and other kernel methods. Based on the data distributions of classes in feature space, we proposed a kernel selection criterion named Kernel distancebased class separability (KDCS) to evaluate the goodness of a kernel in multiclass classification scenario. KDCS is differentiable with respect to the kernel parameters, thus the gradient-based optimization technique can be used to find the best model efficiently. In addition, it does not need to put a part of training samples aside for validation and makes full use of all the training samples available. The relationship between this criterion and kernel polarization was also explored. Compared with the 10-fold cross validation technique which is often regarded as a benchmark, this criterion is found to yield about the same performance as exhaustive parameter search.
Abstract: Chinese Named entity recognition (NER) is an important task for Chinese information processing. Traditional sequence labeling approaches to Chinese NER cannot treat globally a string of continuous characters as a named entity candidate so that the entity-level features cannot be exploited in a natural way. To deal with this problem, we formulate Chinese NER as a joint identification and categorization task that performs the two subtasks simultaneously: boundary identification and entity categorization, together with segmentation. The proposed approach provides a natural formulation to treats pieces of continuous characters as named entity candidates, which allows for more accurate prediction by examining both the internal evidence and contextual information of the candidates. Within this framework, we explored a variety of effective feature representations for Chinese NER. Closed tests on two quite different corpora from the third SIGHAN bakeoff show that our approach significantly outperforms the best in the literature, achieving state-of-theart performance.
Abstract: As a new approach for human-computer interaction, the non-contact gaze tracking technology is facing an increasing demand for its practical application in daily interaction. Especially in respect of users with eyeglasses, the gaze tracking that is adaptive to interference of eyeglasses is attracting more attentions. In this paper, a robust algorithm of feature extraction for noncontact gaze tracking with eyeglasses is presented. The algorithm uses a pyramidal multi-scale screening strategy to locate the pupil at first, and then utilizes knowledge of essential characteristics to distinguish the real cornealreflections from interference of eyeglasses. On this basis, the feature extraction of gaze is more robust and accurate. An experiment has been taken to evaluate the algorithm's performance to the interferences in some different situations. Comparing to a former algorithm that has been used, the success rate of the proposed algorithm has risen significantly. Therefore, the algorithm exhibits great potential for practical applications.
Abstract: Recently, a novel formalism named Twodimensional description logics is provided for representing and reasoning about contextualized knowledge. Following achievements of the research, a metamodel for a Two-dimensional description logic is provided in this paper. Using Model driven architecture (MDA) technologies, a prototype of ontology modeling tool is generated from the metamodel. Metamodels for variants in the family of Two-dimensional description logics are also discussed comparatively. Further more, based on these metamodels, a metrics definition approach for Two-dimensional description logics is also provided.
Abstract: To conquer the slow convergence and poor scalability problems of reinforcement learning, a Scalable parallel reinforcement learning method, DCS-SPRL, is proposed on the basis of Divide-and-conquer strategy. In this method, the learning problem with large state space is decomposed into multiple smaller subproblems. According to a weighted priority scheduling algorithm, these subproblems are then dispatched to the learning agents which are able to learn in parallel. Finally, the learning results of each subproblem are merged into a composite solution. The experimental results show that DCS-SPRL has good scalability and needs significantly less computational time.
Abstract: Recently, serious food safety events have emerged frequently and food safety issues have caused wide public concern all over the world. To find the needed food safety information for users from the growing information over the Internet, this paper presents an ontology-based semantic retrieval model used in food safety domain information retrieval. Firstly, food safety domain ontology is constructed to represent food safety domain knowledge, which has many advantages, such as sharable, reusable, and scalable. So it is very appropriately to describe the semantic relationships between food safety domain concepts. Then the lexicon for words segmentation is expanded based on the food safety domain ontology to return more accurate preprocessing results of queries. Finally semantic query expansion and sorting algorithm of search results based on concepts similarity computation model are implemented so that more relevant results can come before irrelevant retrieval results. The experiments show that the precision of the retrieval method with the proposed model is higher 25.2% averagely than that of the traditional retrieval method.
Abstract: Aiming at dynamic coordination at runtime, the paper proposes recursive coordination architecture for multi-level hierarchical information systems. It describes the element of system as a self-managed structure, including computing unit, coordinator and coordination space. The computing unit consists of role, actors and services. In order to support actors with multitasks and provide fine-grained coordination, we present a service oriented coordination model based on role for the development and integration of multi-level hierarchical information system. The model classifies coordination into required resource-centered coordination and endeavored service-centered coordination. Coordination among services is achieved by means of coordination space at runtime. The model supports computing units at different levels of granularity and has adaptability to change of organization structure and environment. A real example of commodity trading system illustrates practicability of the model.
Abstract: Super long integers which exceed the limit of numbers defined in existent computers are widely employed in cryptosystems. In the paper, design 10 algorithms which address operands by the byte, and are used for the operation of unsigned super long integers, including conversion between a binary number and a decimal one, shift, comparison, addition, subtraction, multiplication, division, and modular power, analyze the time complexity of each of the algorithms in the amount of bit operations, offer the source code of the modular power operation in C, and give some examples which are utilized for validating the correctness of the algorithms according to the properties of a group.
Abstract: This paper investigates the asymptotic stability of genetic regulatory networks with random delays and Markovian jumping parameters. The delay considered here is assumed to be satisfying a certain stochastic characteristic. Corresponding to the probability of the delay taking value in different intervals, stochastic variables satisfying Bernoulli random binary distribution are introduced and a new system model is established by employing the information of the probability distribution. By using a Lyapunov functional approach and linear matrix inequality techniques, the stability criteria for the delayed Markovian jumping genetic regulatory networks are expressed as a set of Linear matrix inequalities (LMIs), which can be solved numerically by LMI toolbox in MATLAB. A genetic network example is given to verify the effectiveness and the applicability of the proposed approach.
Abstract: Interval multi-objective optimization problems (IMOPs) are popular in real-world applications. However, since the optimized objectives not only are multiple but also contain interval parameters, there have been few methods of solving them up to date. We presented a novel method of effectively solving the problems above in this study. In this method, the lower limit of the possibility degree was defined and used to describe a dominance relation of IMOPs. The dominance was further employed to modify the fast non-dominated sorting of Non-dominated sorting genetic algorithm II (NSGA-II). After analyzing its performance, our method was applied to four IMOPs and compared with two typical optimization methods. The experimental results confirmed the advantages of our method.
Abstract: Algebraic immunity measures the resistance of a Boolean function against algebraic attack. To resist algebraic attack, a Boolean function should possess high algebraic immunity. Concatenation is an important method by which we can construct Boolean functions with good cryptographic properties. In this paper, we investigate certain classes of Boolean functions g=f1||f2||f3||f4 for their algebraic immunity. When n is even, we obtain two special classes (n+2)-variable Boolean functions with maximum algebraic immunity. Last, for an odd integer n, we get the divisibility result on the weights of Boolean functions with maximum possible algebraic immunity.
Abstract: Fault diagnosis of airborne equipments is of great significance, while the knowledge for fault diagnosis is hard to acquire. A knowledge acquisition method for fault diagnosis based on support vector regression machine and rough set theory is presented in this work. Due to the redundancy and incompleteness of the original data, we start with applying support vector regression machine to attain support vectors. Then by using Monte Carlo method, we generate new random data around support vectors and build a complete dataset composed of the original data and the new random data. Finally, the rough set method is used to acquire knowledge for fault diagnosis from the complete dataset. The proposed method leads to an increase in accuracy as well as a decrease in uncertainty.
Abstract: The characteristics of Alumina evaporating process (AEP) are analyzed firstly. Operational pattern is defined to describe the AEP. A new framework is proposed, which formulates the operational pattern recognition problem as a multi-class class-imbalanced problem of unequal misclassification costs. Aim to the multiclass class-imbalanced problem of unequal misclassification costs in the operational pattern set of AEP, a multiclass cost-sensitive Probabilistic neural network (mc-PNN) method is proposed. A spectral clustering based on Balanced iterative reducing and clustering using hierarchies (BIRCH) is used to optimize the number of pattern layer nodes of mc-PNN. Experimental results show that the proposed method reduces effectively average misclassification costs and increases recognition rate of excellent and faulty classes.
Abstract: For the point of Region of interest (ROI) extraction influenced by low-level features in images, this paper proposes two ROI extraction algorithms. The first one is based on eye movement data and use proposed “Marking Helm” method. The second one is an optimal weighted feature ROI extraction algorithm based on performance evaluation of feature ROI with different low-level features such as color, intensity, orientation and texture feature in images. By analyzing the similarity between the extracted ROIs by the proposed optimal weighted feature ROI extraction algorithm, visual attention models and eye movement data respectively, this paper confirms the validity of the proposed algorithm. Experimental results show that the proposed optimal weighted feature ROI extraction algorithm improves the similarity at least ten percent over the ROI extraction algorithm based on Itti and Stentiford visual attention models.
Abstract: Large vocabulary continuous speech recognition is particularly difficult for low-resource languages. In the scenario we focus on here is that there is a very limited amount of acoustic training data in the target language, but more plentiful data in other languages. We investigate both feature-level and model-level approaches. The first is based on theMLPframework, inwhich we train the multi-streams based on the Automatic speech attribute transcription strategy and data sampling method individually, and a multilingual training mode using the non-target languages data is presented to obtain more discriminative features. At the model level we apply the recently proposed Subspace Gaussian mixture model to obtain more improvement. Finally, combining these two strategies in a multilingual training mode we get a large improvement of more than 13% absolute versus a conventional baseline.
Abstract: In current studies on ERP-based brain computer interface, a big challenge is to find subjectspecified feature combination for more robust classification. In this paper we propose a recursive feature optimization method based on adaptive boosting and support vector machine to select optimal feature combination. The results of ERP-based brain computer spelling experiment on 11 subjects prove that AdaBoost-based optimization method can significantly improve classification accuracy and simultaneously depress feature dimension greatly. Meanwhile the computational complexity of optimization method is simplified by AdaBoost strategy for practical possibility.
Abstract: A new method, TrTF-FrFT, is introduced for parameter estimation of Linear frequency modulated (LFM) signals, which consists of a coarse search and a fine search. In the initial stage, the window length for Short time Fourier transform (STFT) is selected to get the right time-frequency resolution and concentrate the energy to a line in spectrogram according to the LFM signal characteristic, then the line of ridge energy is tracked in the spectrogram, and the parameters of LFM signal can be estimated coarsely. In the second stage, the fine search for correct parameters is implemented in the Fractional Fourier transform (FRFT) domain using Gaussian model to fit the impulse response of the LFM signals under the guidance from the above stage by the property of monotonicity in the limited neighborhood of the optimal value. In the range of the effective SNR from 20dB to -12dB, the accuracy of the parameter estimation are perfect and affected very little by noise with low computational complexity.
Abstract: The theoretical and experimental research on energy changes due to nonlinear wave interactions is presented. The physical principle of wave parametric amplification underwater was analyzed from the view of external force acting. The acoustic amplitude solutions to nonlinear wave interactions were given according to Burgers equation by imitating a three-wave interaction model. The process of the sound energy transfer was simulated and the results demonstrated that sound energy presented a tendency of pulsation variation, either to increase or to decrease. The experimental results of wave energy amplification were given under the condition of wave optimum coupling. Results of the theoretical and experimental research provide a technical guidance for analysis of sound energy changes during nonlinear wave interactions underwater, which shows the feasibility of detecting underwater weak signals through three-wave coupling.
Abstract: The domain concept is one of the key elements of ontology. In order to automatically acquire the domain concepts during the domain ontology construction, we proposed a novel Bootstrapping-based automatic acquisition algorithm of domain concepts. In our work, the compound words are extracted according to the combination conditions of the mutual information and the information entropy. The candidate domain concepts determinant conditions based on the co-occurrence sentences frequency are presented. Besides, to avoid omitting the domain concepts with the lower frequency or semantically similar to other domain concepts, the semantic factor is introduced. The experiments results have demonstrated that the compound domain concepts and the semantically similar domain concepts with the lower sentences frequency can also be extracted by using the proposed algorithm. And the proposed algorithm has obtained higher precision and recall, so it is effective and feasible.
Abstract: In underdetermined blind source separation, in order to quickly and accurately estimate the mixing matrix and the source signals, this paper presents a new algorithm based on ant colony clustering. The basic ideal of the algorithm is that we utilize the linear clustering characteristic of sparse signals to estimate the number of sources and the column vector of the mixing matrix by estimating the directions of the straight lines. In the preprocessing step, the observed signals in time domain are transformed to sparse signals in frequency domain. Through normalizing the observed data the linearity clustering is translated to compact clustering, and then using ant colony clustering to get the number of source signals and the mixing matrix. Finally, based on the estimation of the mixing matrix, the source signals are recovered by linear programming method. The simulation results illustrate the availability and accuracy of the proposed algorithm.
Abstract: Linear discriminant analysis (LDA) cannot be directly applied to Small sample size problem (SSS problem). A new approach called Valid discriminative nullspace (VDNS) based on a variation of Fisher's LDA for the small sample size case is proposed. We revealed the physical meaning of the null spaces of the total scatter matrix and the within-class scatter matrix. We also analyzed the relationships of between-class scatter matrix of three subspaces: the valid subspace, the valid null space, and the valid discriminative null-space. This provides the new approach of subspace analysis-VDNS. VDNS method can project data on a lower dimensional subspace which contains valid discriminative information. Experimental results on different data sets showed that the VDNS method is superior to other relative methods in terms of recognition accuracy, robust and efficiency.
Abstract: Tags or keywords provide an efficient way to manage and retrieve large scale data. This paper proposes an unsupervised method to suggest informative tags for multi-party dialogues by integrating dialogue characteristics. Our model first extracts keywords from dialogue texts under a speaker salience based framework. Then we get keyword bigrams through frequent pattern matching. In order to generate more flexible and meaningful tags, we expand keywords and their bigrams by tag association rules mined from a popular bookmarking web del.icio.us. Finally we rank the three types of tag candidates under a uniform metric. Experimental results validate the effectiveness and the versatility of our method when compared with several strong baseline models like TextRank, TFIDF rank and KNN.
Abstract: Clustering analysis is an effective technique for exploring data analysis which has been widely applied to varied tasks. Many classical clustering algorithms do good jobs on their prerequisite, but few of them are scalable when applied to Very large data sets (VLDS). In this study, a novel means radial compression clustering method is proposed to deal with the VLDS. First, the concept of means radial compression is defined to describe theoretical model. Next, mean merging is defined and it is proved that the process of mean merging is an efficient method for the implementation of means radial compression. Then, the members will be assigned to the suitable clusters based on the minimum distance between each member and the centers that is found by means radial compression clustering. The experimental results show that means radial compression algorithm can make better solutions compared with the most well known clustering algorithms as K-means clustering, affinity propagation clustering, hierarchical clustering with time complexity of O(n).
Abstract: Natural scene categorization is a challenging pattern classification problem and the image representation has deep impact on the classification performance. To improve the robustness and effectiveness of the image representation, a novel integrated scheme is proposed. Firstly, a feature combination method is adopted to generate a compound feature which contains the local texture and the spatial structure information for each image. Then an optimized dimensionality reduction algorithm is applied on the compound features to get lower dimensional and compressed feature representations. In the following, the dimensionality reduced features are clustered by a k-means based adaptive clustering algorithmto form a proper visual codebook, and each image is represented by the codebook histogram. Finally, the support vector machine is exploited to do the scene categorization tasks using the robust image representations. The proposed scheme is sufficiently evaluated on three well-known scene datasets. The experimental results show that our proposed method effectively enhances the image representation and outperforms the state-of-the-art approaches.
Abstract: Many vehicle application services called LBS (Location based services) are currently based on location information, but civil location equipment is either too expensive or the positioning accuracy is not high enough to provide LBS. If each vehicle used as a center can precisely calculate the relative positions of other vehicles, LBS can be more easily provided. Based on this premise, a new method by limiting low-cost GPS receivers and communication satellites into similar environment respectively to calculate the relative position with a higher accuracy is proposed in this study.
Abstract: To maximize the average rate in a longterm scope while guarantee primary user's QoS demand is a new key issue in cognitive radio systems. A dynamic programming based power control algorithm with the consideration of primary user's QoS by a rate loss constraint criterion is proposed in this paper. In the proposed algorithm, the occupancy for each subcarrier by primary users is modeled as a discrete-time Markov chain. And the concept of rate loss constraint is defined which is used to guarantee primary user's desired rate. Then the dynamic programming framework with rate loss constraint of this problem is formulated, which is a function of the primary user occupancy state, the system channel gains and the remaining power budgets for the cognitive radio transmitters. At last, we give a solution for the problem. Simulation results show that the proposed algorithm can obtain a maximum average data rate over a finite time horizon and effectively guarantee primary users' QoS.
Abstract: Efficient target localization inWireless sensor network (WSN) relies significantly on the Medium access control (MAC) it implements. TDMA MAC protocol is customarily used in these applications. But it only has good performance in motionless target detection, and can't cope with target-tracking detection. In this paper, a Target-tracking MAC (TT-MAC) protocol is proposed for target-tracking detection in target localization WSN. In this protocol, an energy-based clustering technique is used to achieve target-tracking detection, a sleeping mechanism is proposed for energy conservation, and a tight scheduling mechanism is proposed to reduce the latency. The protocol is compared with the cluster-based MAC protocol on Mica2 platform, and shows its superiority in energy and latency.
Abstract: Opportunistic networks are novel selforganizing network models with the general characteristics of delay tolerant network. The main challenge for these environments is that conventional routing schemes cannot be adopted straightforwardly. In this paper, we propose a Space-aware spray and transfer routing (SSTR) that considers both the temporal and spatial information of the mobile nodes to help nodes to select more competent nodes for further forwarding the copies of packets. The proposed routing makes use of the location, moving speed, encountering interval and encountering duration of nodes to calculate the delivery predictability, and applies it to a novel spray and transfer strategy. Simulation results show that the proposed SSTR routing performs better than other routings, such as Spray and wait, Epidemic and ProPHET, in terms of the delivery rate, the average delay and the communication overhead, and it is wellsuited to the frequently disconnected dense opportunistic network.
Abstract: Cropping is a common form of manipulation, it is indiscernible and hard to detect, since the interception will not destroy most of the image's original features. In this paper, how to detect cropping according to a single image is described, and a new camera calibration algorithm into digital forensics is proposed, which conducts the calibration according to orthometric vanishing points. Our method can estimate the principal point according to a single image, science the vanishing points only depends on the direction of the object. Experiments show that our method can identify asymmetric cropping of the image effectively, and also can estimate the principal points precisely, besides it is robust to smoothing and noise.
Abstract: Combined with Fourier spectrum prior knowledge, a novel wavelet multiresolution analysis and forecasting algorithm is proposed. It focuses on long term trend prediction of multi-periodic, non-stationary, mobile communication traffic series. New algorithm calculates the Fourier spectrum for multi-periodic series at first, and takes the prominent period components with definite physical notion as the prior knowledge. After that, it extracts more valuable time domain features with wavelet multiresolution analysis. Finally, it adopts a single model to predict each of them, and integrates the prediction results to gain the final trend prediction of traffic time series. Experimental results on real traffic data show that all isolated components in proposed multiresolution analysis deliver the distinct physical information in traffic data. Additionally, our algorithm can pick out most of prominent period components revealed in the Fourier spectrum and improve prediction accuracy.
Abstract: In digital communications, the channel affects the transmitted sequence with both linear and nonlinear distortions. We address the blind detection over time-varying frequency-selective and nonlinear channels in this paper. A blind detector is derived within the Bayesian framework and implemented via the Particle filtering (PF) method. The proposed PF-based detector incorporates auxiliary PF strategy and a hybrid importance distribution to improve the efficiency and effectiveness. A delayedweight estimation method is also presented to further improve the performance of the detector. Simulations are provided that demonstrate the performance of the detector under different system settings.
Abstract: In this paper, we propose an anonymous authentication protocol based on group signature under Canetti-Krawczyk (CK) model for mobile roaming networks. The protocol involves only a mobile user and the user's visited server, without the involvement of the user's home server until the user's identity (ID) needs to be revealed. In order to reduce the computational burden on the mobile terminals, the protocol gives a method that minimizes expensive pairing operations. Then, its performance as well as efficiency is analyzed. It is shown that the proposed protocol greatly reduces the average authentication latency while security and anonymity are still preserved. Therefore, it can be applicable in practical applications.
Abstract: This paper presents a robust Multipleinput multiple-output (MIMO) transceiver optimization method based on Tomlinson-Harashima precoding (THP) structure, assuming that statistical imperfect Channel state information (CSI) model with spatial correlation. The design process expresses the Minimum square error (MSE) in terms of only the precoding matrix, and then optimizes the precoding matrix itself. For three scenarios with different spatial correlation information, the lower bound of the MSE is minimized, and the precoding matrix achieving the minimum can actually be chosen such that the lower bound is tight. The robustness and effectiveness of the proposed design is validated by simulations.
Abstract: Aiming at fast mobile communications, the design strategies on a novel family of multicarrier signals, named Lattice orthogonal frequency division multiplexing (LOFDM), whose subcarriers are lattice-tiling on timefrequency plane, are dealt with in this paper under general time-varying multipath channels. Here, it chooses the efficient offset-vector-based model for LOFDM signal description. With its help, the optimizations of the pulse scale and Time-frequency location (TFL) are done on the primary channel case of flat power-scattering profile to minimize the level of symbol crosstalk interferences at receiver. Based on the efforts above, a common result for LOFDM design is derived by extending channels to the ordinary cases of arbitrary power-scattering functions, which is done via Taylor series extension. Finally, among possible solutions the better choice that is preferred in band-limited applications is also considered. All above are supported by theoretic analysis and numerical results.
Abstract: Synthetic aperture radar (SAR) image segmentation is the basis of SAR image analysis and understanding. A segmentation method based on the neighbor and near spatial relationship is proposed to reduce the speckle noises influence and improve the segmentation region regularity. The SAR image is coarsely segmented by the scatter intensity of neighbor pixels. For the unregularity of region in coarse segmentation, a region regularity method based on Run-length grouping (RLG) is proposed. The region regularity method adjusts the short run-length segmentation type with the spatial relationship of near pixels. The segmentation region can be more regularity by connecting the broken region, smoothing the tortuous edge and removing patch.
Abstract: The reasonable design of particle filter framework in multi-sensor observation system is the key to expand the application domain of sampling nonlinear filters. Aiming at the effective realization of particle filter for multi-sensor target tracking problem, a novel average weight optimization Rao-Blackwellised particle filtering algorithm is proposed. Combining with the kinetic equation of target state evolution, RBPF is used as the basic estimator of algorithm realization. For the rational utilization from multi-sensor observations and the reduction of the adverse influence from random observations noise in measuring process of particles weight, the average weight optimization strategy is used to improve the reliability and stability of particle weight variance. In addition, we give the concrete flow of RBPF in average weight optimization strategy. Finally, the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.
Abstract: In ultrasonic nondestructive testing, the reflection sequence always overlapped or immersed in noisy. A sparse deconvolution was proposed to enhance significantly the resolution of the original time trace, making it possible to determine the amplitudes and the arrival times of the wave packets contained in the original time sequence. With the purpose of ensuring sparsity, Compressed sensing (CS) and Orthogonal matching pursuit (OMP) were used for pulse-echo signal reconstruction to obtain sparser distortion function for sparse deconvolution. Simulated results and experimental verification which performed on time of flight diffraction (TOFD) specimens demonstrated the proposed method.
Abstract: 3-D Time domain finite element methods (TDFEMs) based on quadratic B-spline, cubic B-spline temporal basis functions and second order Lagrange interpolation polynomial were presented. We derived the formulations of TDFEM based on the several temporal basis functions. The correctness of these schemes was verified on numerical examples. Finally, comparing accuracy and efficiency of the TDFEM in terms of B-spline basis, Lagrange interpolation polynomial and piecewise linear temporal basis functions, it shows that the proposed schemes satisfy accuracy and time efficiency.
Abstract: To develop compact, reliable and high power terahertz (THz) radiation sources, experimental investigation of the operation at a second harmonic THz gyrotron oscillator is reported in this paper. During operation in five microseconds pulse length regime with 41kV beam voltage and 1A beam current, the instrument generates over 1 kilowatt of power in the second harmonic TE26 mode at 0.423THz. And the designs of the gyrotron operation agree with the experimentally measured output power and Radio-frequency (RF) efficiency. Details of the gyrotron design, operation and measurements of output radiation are given.
Abstract: In this paper, a multi-layer TDS (Thin dielectric sheets) model is proposed to simplify the Volume integral equation (VIE) based on the TDS approximations when we solve the Electromagnetic (EM) scattering from layered dielectric structures. The tangential components of polarization current in each layer are approximated to linear variation along the transverse direction while constant in the normal direction. Similarly, the normal components are approximated to vary linearly in the normal direction and keep constant within a small patch in the tangential direction. Based on these approximations, and the divergence free and normal continuity conditions of D flux, a set of recursive formulations are derived out to reduce the number of independent basis functions, which are used to construct the polarization current. Then, we can further approximate the volume integrals in VIE to some surface integrals for far filed evaluation. The above TDS approximations can alleviate the difficulty of geometry discretization, reduce the number of unknowns, and shorten the time consumption of numerical calculation. The computational complexity of numerical simulation is reduced significantly.
Abstract: Chaos shows many advantages in radar application. A chaos sampled method is proposed in this paper. Two sampling models are provided. We obtain new chaotic series of noise like phase space structures and sharp autocorrelation functions. The model of chaos-based frequency modulated waveform is then put forward and properties including frequency spectrum, ambiguity function are theoretically analyzed in detail. Additionally, a sufficient condition of chaotic behavior sustaining for this frequency modulation is deduced. We confirm that the sampling method can present much superiority in finding suitable or even perfect chaotic series for frequency modulated radar waveform design, and outstanding performance can be obtained as well when the sufficient condition is satisfied. Numerical simulations mainly based on the Bernoulli chaos validate the theoretical analysis.
Abstract: This paper proposed a novel Global positioning system (GPS) receiver adaptive Digital beamforming (DBF) interference suppression algorithm, which remodeled Minimum power distortionless response (MPDR) beamforming by transforming it into Minimum mean square error (MMSE) beamforming in the frame of Generalized sidelobe canceller (GSC), and introduced Kalman filter (KF) to carry out adaptive beamforming. Theory analyses and simulation results indicate that this algorithm has higher output Signal to interference and noise ratio (SINR) than Recursive least square (RLS) and Least mean square (LMS) adaptive beamforming algorithms which are in common use, and similar convergence rate with RLS algorithm.