Wenchao LI Jianyu YANG Yulin HUANG Lingjiang KONG
For Doppler parameter estimation of forward-looking SAR, the third-order Doppler parameter can not be neglected. In this paper, the azimuth signal of the transmitter fixed bistatic forward-looking SAR is modeled as a cubic polynomial phase signal (CPPS) and multiple time-overlapped CPPSs, and the modified cubic phase function is presented to estimate the third-order Doppler parameter. By combining the cubic phase function (CPF) with Radon transform, the method can give an accurate estimation of the third-order Doppler parameter. Simulations validate the effectiveness of the algorithm.
Wei YI Lingjiang KONG Jianyu YANG
Dynamic Programming (DP) based Track-Before-Detect (TBD) algorithm is effective in detecting low signal-to-noise ratio (SNR) targets. However, its complexity increases exponentially as the dimension of the target state space increases, so the exact implementation of DP-TBD will become computationally prohibitive if the state dimension is more than two or three, which greatly prevents its applications to many realistic problems. In order to improve the computational efficiency of DP-TBD, a thresholding process based DP-TBD (TP-DP-TBD) is proposed in this paper. In TP-DP-TBD, a low threshold is first used to eliminate the noise-like (with low-amplitude) measurements. Then the DP integration process is modified to only focuses on the thresholded higher-amplitude measurements, thus huge amounts of computation devoted to the less meaningful low-amplitude measurements are saved. Additionally, a merit function transfer process is integrated into DP recursion to guarantee the inheritance and utilization of the target merits. The performance of TP-DP-TBD is investigated under both optical style Cartesian model and surveillance radar model. The results show that substantial computation reduction is achieved with limited performance loss, consequently TP-DP-TBD provides a cost-efficient tradeoff between computational cost and performance. The effect of the merit function transfer on performance is also studied.
MeiJun DUAN HongYu YANG Bo YANG XiPing WU HaiJun LIANG
Due to its simplicity and efficiency, differential evolution (DE) has gained the interest of researchers from various fields for solving global optimization problems. However, it is prone to premature convergence at local minima. To overcome this drawback, a novel hybrid dragonfly algorithm with differential evolution (Hybrid DA-DE) for solving global optimization problems is proposed. Firstly, a novel mutation operator is introduced based on the dragonfly algorithm (DA). Secondly, the scaling factor (F) is adjusted in a self-adaptive and individual-dependent way without extra parameters. The proposed algorithm combines the exploitation capability of DE and exploration capability of DA to achieve optimal global solutions. The effectiveness of this algorithm is evaluated using 30 classical benchmark functions with sixteen state-of-the-art meta-heuristic algorithms. A series of experimental results show that Hybrid DA-DE outperforms other algorithms significantly. Meanwhile, Hybrid DA-DE has the best adaptability to high-dimensional problems.
Qian LIU Chao LAN Xiao Yuan JING Shi Qiang GAO David ZHANG Jing Yu YANG
In the past few years, discriminant analysis and manifold learning have been widely used in feature extraction. Recently, the sparse representation technique has advanced the development of pattern recognition. In this paper, we combine both discriminant analysis and manifold learning with sparse representation technique and propose a novel feature extraction approach named sparsity preserving embedding with manifold learning and discriminant analysis. It seeks an embedded space, where not only the sparse reconstructive relations among original samples are preserved, but also the manifold and discriminant information of both original sample set and the corresponding reconstructed sample set is maintained. Experimental results on the public AR and FERET face databases show that our approach outperforms relevant methods in recognition performance.
Junjie WU Jianyu YANG Yulin HUANG Haiguang YANG Lingjiang KONG
With appropriate geometry configurations, bistatic Synthetic Aperture Radar (SAR) can break through the limitations of monostatic SAR for forward-looking imaging. Thanks to such a capability, bistatic forward-looking SAR (BFSAR) has extensive potential applications. This paper develops a frequency-domain imaging algorithm for translational invariant BFSAR. The algorithm uses the method of Lengendre polynomials expansion to compute the two dimensional point target reference spectrum, and this spectrum is used to perform the range cell migration correction (RCMC), secondary range compression and azimuth compression. In particular, the Doppler-centroid and bistatic-range dependent interpolation for residual RCMC is presented in detail. In addition, a method that combines the ambiguity and resolution theories to determine the forward-looking imaging swath is also presented in this paper.
Hongwei DAI Zheng TANG Yu YANG Hiroki TAMURA
Immune system protects living body from various attacks by foreign invades. Based on the immune response principles, we propose an improved lateral interaction artificial immune system model in this paper. Considering that the different epitopes on the surface of antigen can be recognized by a set of different paratopes expressed on the surface of immune cells, we build a neighborhood set that consists of immune cells with different affinities to a certain input antigen. We update all the weights of the immune cells located in neighborhood set according to their affinities. Simulations on noisy pattern recognition illustrate that the proposed artificial immune system model has stronger noise tolerance ability and is more effective at recognizing noisy patterns than that of our previous models.
Haijun LIANG Hongyu YANG Bo YANG
A new paradigm for building Virtual Controller Model (VCM) for traffic flow simulator is developed. It is based on flight plan data and is applied to Traffic Flow Management System (TFMS) in China. The problem of interest is focused on the sectors of airspace and how restrictions to aircraft movement are applied by air traffic controllers and demand overages or capacity shortfalls in sectors of airspace. To estimate and assess the balance between the traffic flow and the capacity of sector in future, we apply Virtual Controller model, which models by the sectors airspace system and its capacity constraints. Numerical results are presented and illustrated by applying them to air traffic data for a typical day in the Traffic Flow Management System. The results show that the predictive capabilities of the model are successfully validated by showing a comparison between real flow data and simulated sector flow, making this method appropriate for traffic flow management system.
Chao XU Yunfeng YAN Lehangyu YANG Sheng LI Guorui FENG
The altered fingerprints help criminals escape from police and cause great harm to the society. In this letter, an altered fingerprint detection method is proposed. The method is constructed by two deep convolutional neural networks to train the time-domain and frequency-domain features. A spectral attention module is added to connect two networks. After the extraction network, a feature fusion module is then used to exploit relationship of two network features. We make ablation experiments and add the module proposed in some popular architectures. Results show the proposed method can improve the performance of altered fingerprint detection compared with the recent neural networks.
Yi WANG Liang DONG Taotao LIANG Xinyu YANG Deyun ZHANG
Routing algorithms with low overhead, stable link and independence of the total number of nodes in the network are essential for the design and operation of the large-scale wireless mobile ad hoc networks (MANET). In this paper, we develop and analyze the Cluster Based Location-Aided Routing Protocol for MANET (C-LAR), a scalable and effective routing algorithm for MANET. C-LAR runs on top of an adaptive cluster cover of the MANET, which can be created and maintained using, for instance, the weight-based distributed algorithm. This algorithm takes into consideration the node degree, mobility, relative distance, battery power and link stability of mobile nodes. The hierarchical structure stabilizes the end-to-end communication paths and improves the networks' scalability such that the routing overhead does not become tremendous in large scale MANET. The clusterheads form a connected virtual backbone in the network, determine the network's topology and stability, and provide an efficient approach to minimizing the flooding traffic during route discovery and speeding up this process as well. Furthermore, it is fascinating and important to investigate how to control the total number of nodes participating in a routing establishment process so as to improve the network layer performance of MANET. C-LAR is to use geographical location information provided by Global Position System to assist routing. The location information of destination node is used to predict a smaller rectangle, isosceles triangle, or circle request zone, which is selected according to the relative location of the source and the destination, that covers the estimated region in which the destination may be located. Thus, instead of searching the route in the entire network blindly, C-LAR confines the route searching space into a much smaller estimated range. Simulation results have shown that C-LAR outperforms other protocols significantly in route set up time, routing overhead, mean delay and packet collision, and simultaneously maintains low average end-to-end delay, high success delivery ratio, low control overhead, as well as low route discovery frequency.
Hongwei DAI Yu YANG Cunhua LI Jun SHI Shangce GAO Zheng TANG
Clonal Selection Algorithm (CSA), based on the clonal selection theory proposed by Burnet, has gained much attention and wide applications during the last decade. However, the proliferation process in the case of immune cells is asexual. That is, there is no information exchange during different immune cells. As a result the traditional CSA is often not satisfactory and is easy to be trapped in local optima so as to be premature convergence. To solve such a problem, inspired by the quantum interference mechanics, an improved quantum crossover operator is introduced and embedded in the traditional CSA. Simulation results based on the traveling salesman problems (TSP) have demonstrated the effectiveness of the quantum crossover-based Clonal Selection Algorithm.
Junjie WU Jianyu YANG Yulin HUANG Haiguang YANG Lingjiang KONG
Bistatic synthetic aperture radar (BSAR) with one fixed station (OF-BSAR) can be used in wide area surveillance, ground moving target indication etc. This paper analyzes the spatial variance of OF-BSAR. Analytical expressions of the spatial invariance region in the data space are given. Using these results, we can determine the spatial invariance region in the data set and the imaging area. After that, we give a data blocking scheme for raw data focusing. Numerical simulation verifies the results of this paper.
Due to heavy rendering load and unstable frame rate when rendering large terrain, this paper proposes a geometry clipmaps based algorithm. Triangle meshes are generated by few tessellation control points in GPU tessellation shader. ‘Cracks’ caused by different resolution between adjacent levels are eliminated by modifying outer tessellation level factor of shared edges between levels. Experimental results show the algorithm is able to improve rendering efficiency and frame rate stability in terrain navigation.
Chen-Yu YANG Zhen-Hua LING Li-Rong DAI
In this paper, an automatic and unsupervised method using context-dependent hidden Markov models (CD-HMMs) is proposed for the prosodic labeling of speech synthesis databases. This method consists of three main steps, i.e., initialization, model training and prosodic labeling. The initial prosodic labels are obtained by unsupervised clustering using the acoustic features designed according to the characteristics of the prosodic descriptor to be labeled. Then, CD-HMMs of the spectral parameters, F0s and phone durations are estimated by a means similar to the HMM-based parametric speech synthesis using the initial prosodic labels. These labels are further updated by Viterbi decoding under the maximum likelihood criterion given the acoustic feature sequences and the trained CD-HMMs. The model training and prosodic labeling procedures are conducted iteratively until convergence. The performance of the proposed method is evaluated on Mandarin speech synthesis databases and two prosodic descriptors are investigated, i.e., the prosodic phrase boundary and the emphasis expression. In our implementation, the prosodic phrase boundary labels are initialized by clustering the durations of the pauses between every two consecutive prosodic words, and the emphasis expression labels are initialized by examining the differences between the original and the synthetic F0 trajectories. Experimental results show that the proposed method is able to label the prosodic phrase boundary positions much more accurately than the text-analysis-based method without requiring any manually labeled training data. The unit selection speech synthesis system constructed using the prosodic phrase boundary labels generated by our proposed method achieves similar performance to that using the manual labels. Furthermore, the unit selection speech synthesis system constructed using the emphasis expression labels generated by our proposed method can convey the emphasis information effectively while maintaining the naturalness of synthetic speech.
Sheng LI Yong-fang YAO Xiao-yuan JING Heng CHANG Shi-qiang GAO David ZHANG Jing-yu YANG
This letter proposes a nonlinear DCT discriminant feature extraction approach for face recognition. The proposed approach first selects appropriate DCT frequency bands according to their levels of nonlinear discrimination. Then, this approach extracts nonlinear discriminant features from the selected DCT bands by presenting a new kernel discriminant method, i.e. the improved kernel discriminative common vector (KDCV) method. Experiments on the public FERET database show that this new approach is more effective than several related methods.
Fang-Biau UENG Li-Der JENG Jun-Da CHEN Jia-Yu YANG
In direct-sequence code division multiple access (DS/CDMA) multiuser communication systems with multipath channels, both intersymbol interference (ISI) and multiple-access interference (MAI) must be considered. The multipath effect usually changes the characteristics of the spreading codes. Modification of the conventional receiver structure is needed to account for the interference of the multipath fading. This paper proposes four adaptive receivers for such multiuser DS/CDMA systems in multipath fading channels. We employ least mean square (LMS) and recursive least squares (RLS) algorithms for both finite impulse response (FIR) and infinite impulse response (IIR) receiver structures. Mean square error (MSE) and convergence analysis are also given in this paper. Simulation results show the performance comparisons of the four proposed receivers.
Guolong CUI Lingjiang KONG Xiaobo YANG Jianyu YANG
This letter focuses on the performance analysis on the Adaptive Sidelobe Blanker (ASB) detection algorithm in homogeneous environments, and provides closed summation expressions for Probability of Detection (PD) and Probability of False Alarm (PFA) rate in terms of hypergeometric function. The derived results are more powerful and effective than previous integral ones. Moreover, the framework can be modified to solve the the performance analysis problem involving in F or/and beta distributions. Several numerical evaluations of the convergence rate and computation time are provided and discussed.
Yue TAN Wei LIU Zhenyu YANG Xiaoni DU Zongtian LIU
Event-centered information integration is regarded as one of the most pressing issues in improving disaster emergency management. Ontology plays an increasingly important role in emergency information integration, and provides the possibility for emergency reasoning. However, the development of event ontology for disaster emergency is a laborious and difficult task due to the increasingly scale and complexity of emergencies. Ontology pattern is a modeling solution to solve the recurrent ontology design problem, which can improve the efficiency of ontology development by reusing patterns. By study on characteristics of numerous emergencies, this paper proposes a generic ontology pattern for emergency system modeling. Based on the emergency ontology pattern, a set of reasoning rules for emergency-evolution, emergency-solution and emergency-resource utilization reasoning were proposed to conduct emergency knowledge reasoning and q.
Peng YANG Yu YANG Puning ZHANG Dapeng WU Ruyan WANG
The integration of social networking concepts into the Internet of Things has led to the Social Internet of Things (SIoT) paradigm, and trust evaluation is essential to secure interaction in SIoT. In SIoT, when resource-constrained nodes respond to unexpected malicious services and malicious recommendations, the trust assessment is prone to be inaccurate, and the existing architecture has the risk of privacy leakage. An edge-cloud collaborative trust evaluation architecture in SIoT is proposed in this paper. Utilize the resource advantages of the cloud and the edge to complete the trust assessment task collaboratively. An evaluation algorithm of relationship closeness between nodes is designed to evaluate neighbor nodes' reliability in SIoT. A trust computing algorithm with enhanced sensitivity is proposed, considering the fluctuation of trust value and the conflict between trust indicators to enhance the sensitivity of identifying malicious behaviors. Simulation results show that compared with traditional methods, the proposed trust evaluation method can effectively improve the success rate of interaction and reduce the false detection rate when dealing with malicious services and malicious recommendations.
Kuei-Cheng LIN Tsung-Yu YANG Kuan-Yu CHEN Hwann-Kaeo CHIOU
A high efficiency SiGe HBT differential power amplifier with an open collector adaptive bias was successfully demonstrated. A novel linearizer consists of an open collector heterojunction bipolar transistor bias circuit and an MOS feedback diode was proposed, which achieved better power added efficiency (PAE) than that of traditional adaptive bias circuits. The size effect of linearizer was investigated and the impedance ratio (R1/R2) between the linearizer and the main amplifier was optimized by the factor of 3. The measured differential power amplifier achieved an output 1-dB compression point (P1 dB) of 18.7 dBm with PAE of 31.2%, the output second order intermodulation point (OIP2) of 59 dBm, and third-order intermodulation point (OIP3) of 28 dBm. Compared to traditional adaptive bias technique, the proposed linearizer power amplifier effectively improved the PAE. The fabricated die size including pads is less than 0.925 mm2 and suitable for highly integrated linear drive amplifier.
Guolong CUI Lingjiang KONG Xiaobo YANG Jianyu YANG
This letter mainly deals with the multi-rank signal detecting problem against Spherically Invariant Random Vector (SIRV) background with Invariance theory. It is proved that generalized likelihood ratio test (GLRT), Rao test and Wald test are all the Uniformly Most Powerful Invariant (UMPI) detectors in SIRV distributions under a mild technical condition.