The first paper of this series of articles revealed that Four-Color Conjecture is hopefully proved mathematically by investigating a special class of graphs, called the 4-chromatic-funnel, pseudo uniquely-4- colorable maximal planar graphs. To characterize the properties of such class of graphs, a novel technique, “extending-contracting operation”, is proposed which can be used to construct maximal planar graphs. The essence of this technique is to study a special kind of configurations, domino configurations. In this paper, a necessary and sufficient condition for a planar graph to be a domino configuration is constructively given, on the basis of which it is proposed to construct the ancestor-graphs and descendent-graphs of a graph. Particularly, it is proved that every maximal planar graph with order n(≥9) and minimum degree ≥4 has an ancestor-graph of order (n-2) or (n-3) . Moreover, an approach is put forward to construct maximal planar graphs recursively, by which all maximal planar graphs with order 6~12 and minimum degree ≥4 are constructed. The extending-contracting operation constitutes the foundation in this series of articles.

A maximal planar graph is called the recursive maximal planar graph if it can be obtained from K₄ by embedding a 3-degree vertex in some triangular face continuously. The uniquely 4-colorable maximal planar graph conjecture states that a planar graph is uniquely 4-colorable if and only if it is a recursive maximal planar graph. This conjecture, which has 43 years of history, is a very influential conjecture in graph coloring theory after the Four-Color Conjecture. In this paper, the structures and properties of dumbbell maximal planar graphs and recursive maximal planar graphs are studied, and an idea of proving the uniquely 4-colorable maximal planar graph conjecture is proposed based on the extending-contracting operation proposed in this series of article (2).

There is a contradiction between the degree of survivability and the cost of deployment in Hybrid Optical-Wireless Broadband Access Networks (HOWBAN). To improve the network resources utilization, a low- cost Optical Network Unit (ONU) deployment strategy is proposed. According to the deployment of wireless functions in the network, the degree of network survivability and the deployment are considered in the optimization process of vector evaluated binary particle swarm optimization. Furthermore, to deploy the network with low cost while at the same time guaranteeing a high degree of survivability, the selecting of optical-network-units can be decided dynamically by the evolution of particle. The results show that low-cost ONU deployment strategy can effectively improve the network survivability, and reduce network cost.

Complexity of wireless environment often poses high bit error problems. For that reason, and also concerning about the great demands on transmission energy consumption for the WSN, a Maximum A Posteriori method based on Bayesian Network (MAP-BN) is proposed for fixing the wrong protocol fields in packets, which is also according to the transmission characteristics of WSN. MAP-BN makes the nodes in WSN have the ability of forward-error-correction without any prior coding before transmitting a packet. To achieve that, Bayesian Network is used to modeling prior information of protocol fields in packets, and Maximum A Posteriori inference which involved a dynamic programming algorithm is used to reduce the computational complexity successfully. The simulation results show that MAP-BN performance is pretty good in the aspect of error control and energy-efficiency.

The optimal Web service selection based on QoS is still a hot issue. Highly dynamic QoS data leading to uncertainty QoS model is a huge challenge for reliable Web service selection. This paper presents Dynamic QoS Data-driven Reliable Web Service Selection (DQoS_RSS). First, DQoS_RSS uses mean and standard deviation to portray the benefit and risk of QoS and to improve the accuracy of QoS description. Then, the uncertain service Skyline set is built to reduce the search scope, to improve the efficiency of Web service selection. Drown on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) theory, 2 kinds of service selection algorithm are designed to obtain the optimal Web service reflecting user’s QoS needs. In addition, 2 kinds of QoS model converter are introduced to convert QoS data to QoS model; and the QoS model adaptive adjustment mechanism is introduced too, which can adapt to the dynamic changes of QoS. Finally, some experiments demonstrate the superiority and efficiency of the presented approach.

For an onboard switching, serious decline in the reliability is induced by the harsh space radiation environment. In this paper, a 3-stage Clos-network supporting fully distributed scheduling and a Fully Distributed Fault Tolerant (FDFT) scheduling algorithm are proposed to improve fault-tolerant ability of an onboard switching. Combined input and output queued architecture is employed in the central and output stages of the proposed Clos-network to support fully distributed scheduling in both the network and switching elements. In FDFT, a distributed fault detection algorithm is employed to obtain the crosspoint fault information. Based on the analysis of the influence of the faults, a fault-tolerant cell dispatching algorithm is proposed in the input stage which achieves load-balancing to fault-free paths. Theoretical analysis demonstrates that 100% throughput is achieved when no more than (m-n) crosspoint faults occur in any input/output module or in all central modules, where m and n are the number of inputs and outputs of input module, respectively. Furthermore, simulation results indicate that, in the case of faults occurring randomly, FDFT tolerates much more faults, and exhibits a good performance in terms of throughput and average cell delay under different traffic scenarios.

This paper proposes an Eigen domain Transmission Scheme (ETS) to enhance the capacity of MIMO systems under NarrowBand Interference (NBI). First, the transmitter generates N branches of data streams in the eigen domain, then, according to the statistical characteristic of the NBI, the eigen domain signal is transformed into the time domain for transmission. At the receiver, the received time domain signal is transformed into the eigen domain again. Thereafter, the desired channel is divided into N parallel subchannels, to maximize the capacity of the interference channel, the transmission scheme of each subchannel is determined by the power distribution of the NBI. Simulations show that when the capacity is 15 bit/(s?Hz) , the proposed eigen domain transmission scheme has about 10 dB gain over the traditional interference rejection algorithm in the 2×2 MIMO system.

Spectrum sensing is a key technology in the cognitive radio network, in order to protect the primary user, the sensing algorithms must have a high detection efficiency and detection accuracy. This paper mainly focuses on the spectrum sensing in MIMO environment. Considering that the non-circular signal is usually used in the communication system, a novel spectrum sensing method is proposed for non-circular signals based on the Locally Most Powerful Invariant Test (LMPIT). The theoretical threshold is derived according to the asymptotic distribution theorem. Finally, the detection performance comparisons with other methods in various channels are simulated respectively. The results show that the proposed method outperforms other algorithms and only need small sample numbers, thus having higher sensing accuracy and efficiency.

For high speed and high dynamic receiver, using long pseudo-noise code, seriously affected by Doppler frequency offset, this paper proposes a double dwell pseudo-noise code acquisition method based on Compressed Code Phase Correlator (CCPC) and FFT. In the first dwell search a rapid and rough compressed search is performed for some neighboring code phase using CCPC, and at the same time the parallel search for Doppler frequency offset using FFT is completed. In the second dwell, all the neighboring code phases acquired in the first dwell are searched accurately using conventional correlator. The theoretical performance analysis model for the proposed method is presented, whose correctness is validated by Monte Carlo simulation. The simulation result shows that the proposed method has obvious advantages compared to Two-Dimensional Compressed Correlator (TDCC) on Mean Acquisition Time (MAT), as well as on the bandwidth and precision of Doppler frequency. Less resources are consumed than other methods based on FFT when using long pseudo-noise code.

Revocable attribute-based encryption is an extension and generalization of attribute-based encryption. In this paper, a revocable key-policy attribute-based encryption scheme is constructed with two revocation lists, it extends a previous scheme which is designed with only one attribute revocation list, and in the new scheme two attribute revocation lists are involved, and the two lists are independent with each other. What’s more, the new scheme enjoys an important property that the trace algorithm return the user associated with this decryption key. Finally, under the assumption of decisional Bilinear Diffie-Hellman Exponent (BDHE), the proposed scheme is proved that is secure in the selective security model.

Privacy-preserving technology is the focus of information security area. Unfortunately, rare implementation of private set union protocol is developed. To solve the issue above, a novel private set union protocol based on the YAO’s garbled circuit technology is presented. The specially designed circuits include the private set merge circuit, the private set filter circuit and the private set confusion circuit. Then, the security of the novel protocol is proven in semi-honest model. Finally, a prototype of the protocol is built based on the MightBeEvil framework. The simulation results show that this protocol is more efficient than the existing one when evaluating the union of sparse sets in a privacy-preserving manner.

An effective Sparse Bayesian Learning algorithm exploiting Complex sparse Temporal correlation (CTSBL) is proposed in this paper, which is used to recover sparse complex signal. By exploiting the fact that the real and imaginary components of a complex value share the same sparsity pattern, it can improve the sparsity of the estimated signal. A multitask sparse signal recovery issue is transformed to a block sparse signal recovery issue of a single measurement by taking full advantage of the internal structure information among the multiple measurement vector signals. The experiments show that the proposed algorithm CTSBL achieves better recovery performance compared with the existing Complex MultiTask Bayesian Compressive Sensing (CMTBCS) algorithm and BOMP algorithm.

In the case that the jammer platform rotates or is pointed in all different directions, a new scenario consisting of orthogonal multiple elements is presented, and then rigorous mathematical derivation and theoretic analysis are given for the new scenario. The closed-form solutions for angle error are derived, which is useful in mathematics for comparison and evaluation. Meanwhile, it provides theoretical references for optimizing the performance. Through comparisons, the nature of mathematics for orthogonal arrays and linear arrangement of jamming elements are proposed. Using stability factor and angle error that the systems in the same radar pointing angle as index, mathematics analysis and example are made to prove that the new scenario outperforms the conventional one in the aspect of stability and effectivity.

Many researches confirmed the excellent performance of Iterative Adaptive Approach (IAA), when it is applied to spectrum analysis of missing data. Simulation results show that the IAA can use 20 percent of the data to recover the missing samples, which is superior to Gapped Amplitude and Phase EStimation (GAPES). But the reconstruction performance of IAA degrades rapidly when the missing data exceed 80%. This paper introduces a novel method of missing data spectrum analysis, and a relevant modified method of time-domain reconstruction is proposed, called Missing SParse Iterative Covariance-based Estimation(M-SPICE). This method converts the weighted missing data covariance fitting cost function to a convex optimization problem. The global convergence property is obtained by adopting cyclic minimizers. The time-domain reconstruction method is modified by renewing estimation operator, which increases the accuracy of the data reconstruction in the case of underestimation. The simulation indicates that the novel method can be used to estimate the missing data spectrum, and reconstruct missing data accurately, with even fewer valid samples, regardless of gapped or arbitrary missing patterns.

This paper proposes a new Joint Probabilistic Data Association algorithm based on All-Neighbor Fuzzy Clustering (ANFCJPDA) for mutitarget tracking in the clutter. Firstly, distance measure is established according to measurements distribution in validation area and data correlation rules. Then, the predicted position is set up as a cluster center, and the association probabilities are calculated on the basis of fuzzy clustering, which are used as weights to update targets’ state and the covariance. Simulation results show that the proposed method reduces highly the computational complexity compared to conventional Joint Probabilistic Data Association (JPDA) technique, and is effective for multiple target tracking in a cluttered environment.

In the system of auto-tracking receiver, the coherent integration is needed to increase the signal-to-noise ratio so that the angle measuring accuracy can be obtained satisfactorily when signal-to-noise ratio is low. The traditional method to improve the signal-to-noise ratio is the code-acquisition algorithm in channels respectively, which needs to do the two-dimensional searching both in the time and in Doppler dimension. The third dimension bit jump searching is needed when necessary. Therefore, the computation is huge and it can not be used in fast angle estimation cases. In this paper, a kind of coherent integration angle measuring algorithm based on cross- correlation between channels is proposed. The new algorithm has the characteristics of simple structure, low computation, almost zero delay. Meanwhile, the angle measuring accuracy of the new algorithm can be up to the level of the traditional method, and thus can meet the auto-tracking receiver requirement easily. The theoretical derivation and simulation results verify the effectiveness of the new algorithm.

In order to solve the issue of high range sidelobe level of LFM noise radar waveform, a new design method of low sidelobe level LFM noise radar waveform is presented, which is a combination of low sidelobes level waveform design method and LFM noise radar waveform design method. Firstly, the objective function of the low sidelobes level optimization problem is established, and the relation between the quadratic phase factor and random phase factor is used as constraint functions. Then, to solve the optimization problem with constraint functions, Modified Cycle Algorithm New (MCAN) is proposed, which can be solved by iterative algorithm. Finally, simulation results show that this algorithm can effectively suppress range-Doppler sidelobe level, and keep excellent performance in stationary targets and movement targets scenario, it also possesses low probability of intercept.

A novel algorithm for high-speed maneuvering target detection and parameter estimation is proposed. Firstly, the second-order Keystone Transform (KT) is utilized to remove the quadric coupling between the range frequency and the slow time, after that, the Symmetric Instantaneous Autocorrelation Function (SIAF) is calculated. Secondly, in order to achieve energy accumulation, Scaled Inverse Fourier Transform (SIFT), Scaled FT (SFT), and Fast FT (FFT) are successively performed on the different dimensions of the SIAF to obtain a new parameter space, then peak detection is carried out to achieve the estimation of radial velocity ambiguity integer and radial acceleration. Finally, a compensation function is constructed to compensate the range migration and the Doppler spread, then the KT algorithm is employed to realize target detection and the estimation of target’s range and ambiguous radial velocity, with the radial velocity ambiguity integer and ambiguous radial velocity, the unambiguous radial velocity can be calculated. Since the brute-force searching procedure is eliminated, moreover, the SIFT and the SFT can be implemented with the FFT operation, the computational complexity of proposed algorithm is greatly reduced. The simulation results demonstrate the effectiveness of the proposed algorithm.

The existing methods of altitude measurement for low-angle targets adopt the specular reflection surface model, and the direct and multipath signals are considered as two correlated far-field point sources. However, in reality, the wavefront of multipath signal is distorted by irregular reflection surface, and the far-field point source model is not enough to describe the multipath signal. To deal with this model mismatch problem, the low-angle multipath model is mainly studied. This paper begins with a discussion of classical multipath model and is followed by the inversion method of reflection coefficient and the height of reflection surface. Then the perturbation of the multipath signal caused by irregular reflection surface is modeled as perturbational reflection coefficient and a perturbational multipath model is developed with a maximum likelihood method to invert the proposed parameter. Simulation data processing results validate the effectiveness of the inversion method. The effectiveness of the proposed model and inversion method are validated by measured data processing results. These research results can provide valuable information for enhancing the applicability of the low-angle altitude measurement method in practical situations.

In the Three Dimension (3D) imaging using a wideband Multiple-Input Multiple-Output (MIMO) radar, the resolution in the two cross-range dimensions is usually not satisfactory in practice, limited by the length of the MIMO radar array. In the paper, the Compressive Sensing (CS) theory is applied to realize the super resolution in the two cross-range dimensions. Firstly, a joint two dimensions super resolution method via Kronecker CS (KCS) is proposed, to avoid losing the coupling information among different dimensions, which will happen when the super resolution is just considered in each dimension separately. Then, in order to solve the problem of huge storing and computing burden in KCS, a dimension reduction method is proposed further by utilizing the prior information of the low resolution 3D image. Finally, the validity of the method is verified with simulated data and real measured data experiments.

The Fast Factorized Back-Projection Algorithm (FFBPA) can reconstruct images in low sampling rate in Local Polar Coordinates (LPC). However, massive 2 dimensional image interpolations are required in image fusion from different LPCs. Image fusion is much easier in Cartesian Coordinates (CC), whereas, the Nyquist sampling rate of images in CC is higher, resulting in decline in the efficiency. To solve this problem, a spectrum compressing method is proposed. By compressing in range-time domain and range-frequency domain, the azimuth spectrum is greatly compressed. The image quality of the proposed method is similar to that of Back-Projection Algorithm (BPA) and is superior to that FFBPA. This method can also be used in SAR of nonlinear track. In the end, the validity of this method is proved by spaceborne SAR simulation data of 0.1 m resolution and airborne SAR real data of 0.2 m resolution.

To deal with the issue of low oil tanks recognition rate in optical remote sensing image, an improved oil tanks detection method is proposed, which is based on the improved visual saliency model and quasi-circular shadow region. Firstly, the oil tanks are separated from the complex background by using the improved visual saliency model. Secondly, the circular shadow regions are finely detected, and the suspected oil tanks are obtained. Then, the shadow region and the preliminary detection result of oil tanks are obtained. Finally, the false oil tank targets are removed and oil depots are determined based on graph search strategy and prior knowledge. The proposed method is robust to the oil tanks in the optical remote sensing images, and can effectively detect the oil tanks in high recognition rate. The experimental results indicate that the proposed algorithm are fast and accurate to detect the oil tanks, which is suitable for optical remote sensing images of different spatial resolutions.

Both GPS Computerized Ionosphere Tomography (CIT) and BackScatter Ionosonde (BSI) can provide two-dimensional electron density profile in large scale. Based on the result of three separate vertical ionosonde, GPS computerized ionosphere tomography and backscatter ionosonde, this paper compares the two-dimensional electron density profiles of GPS computerized ionosphere tomography and backscatter ionosonde. The results show that both methods reconstruct two-dimensional electron density profiles very well and reflect spatial structure of ionosphere. These two methods show high accuracy in the biggest electron density and peak height of F2 layer in contrast with the result of Vertical Ionosonde (VI).

Moving object detection is a challenging issue in computer vision. In this paper, a new detection method via superpixels is proposed based on spatiotemporal multi-cues fusion. First, the current frame is segmented into a set of superpixels using simple linear iterative clustering and the subblocks of foreground superpixels containing motion information are captured according to the time-varying cue of inter-frame pixel-level. Then, a target model of the previous frame, which is established on the basis of the consistency principle of motion target and space clues of a target, are combined to further determine the detection window including the moving object. Finally, the problem of object detection is converted to object segmentation and an object is divided from the detection window utilizing the dense corner detection. Experimental results using several challenging public video sequences show the effectiveness and superiority of the proposed method compared with other state-of-the-art detection approaches.

Traditional airport noise prediction models are insufficient for their high modeling cost and poor practicability. In this paper, the time series phase space reconstruction theory is introduced, and a novel integrated airport noise prediction model based on fast extreme learning machine and differential evolution is proposed. In the proposed model, the airport noise time series is reconstructed based on the phase space reconstruction theory, and the fast extreme learning machine is used to model the reconstructed phase space vector. Meanwhile, an improved differential evolution algorithm is adopted to search for the optimal parameter combination of phase space reconstruction parameter and model parameter simultaneously. The whole modeling process of the integrated prediction model is very simple and?efficient without any manual?intervention. Experimental results demonstrate that the proposed model can track the variation tendency of airport noise well and can achieve much more accurate prediction results than its counterparts.

In many navigation application scenarios, e.g., emergency rescue, mall shopping, medical guiding, and self-guided tours, the floor plan is regarded as a prior. As a result, these navigation systems are short of scalability and robustness. Focusing on this issue, this paper proposes a dynamic landmark-based method for constructing the indoor map, which mitigates the dependence to a prior with the state-of the-art. First of all, the coarse-grained trajectories are obtained by dead reckoning, meanwhile the static indoor landmarks (such as stairs, elevators, escalators) are captured and dynamic indoor landmarks (three kinds of inflection points) are identified by inertial data. Secondly, the trajectory are constructed calibration and integration is leveraged to implement the indoor pathway mapping. Finally, constructing extensive experiments by means of the prototype to evaluate the proposed design. The analysis results demonstrate that the maximum error and average error are 1.90 m and 0.90 m respectively. 

For feature representation of pedestrian recognition, a hybrid hierarchical feature representation method which combines representation ability of the bag of words model and depth layered with learning adaptability is presented. This method first uses HOG local descriptor gradient-based for local features extraction, and then encoding the feature by a depth of layered coding method, the layered coding method by spatial aggregating Restricted Boltzmann Machine (RBM). For each coding layer, the sparse and selective regularization are used for the unsupervised RBM learning and supervision fine-tuning is used to enhance the visual features representation in classification task. Finally, high-level image feature representation is obtained by the maximum pool and space of Pyramid method, and then the linear support vector machine is used for pedestrian recognition, feature extraction of depth architecture. It improves effectively the accuracy of subsequent recognition. Experimental results show that the proposed method has a high recognition rate.

In this paper, a novel ground atmospheric electric field sensor based on MEMS electric field sensing chip is presented, which resolves the problems of motor wear, high power consumption and failure rate of the conventional electric field mill. The chip is fabricated in a commercial SOIMUMPS process, and the area of this chip is only 5.5 mm×5.5 mm. A weak signal detection method for the chip is proposed. The sensor overall structure scheme is designed, and the sensitivity model is established. The minimum detectable electric field of the sensor is 10 V/m with an uncertainty of 0.67% in the range of -50 kV/m～50 kV/m, and its power consumption is only 0.62 W. The outdoor test shows that the plotted data of the sensor agrees well with those of USA Campbell field mill on both sunny days and thunderstorm days, which indicates that the developed sensor can meet the requirement of lighting monitoring and early warning.

Physical Unclonable Functions (PUF) exploits process variation across the same structure circuits during the manufacturing processes to generate numerous unique, random and unclonable security keys. In this paper, a multi-port configurable PUF scheme is proposed, which is based on random deviation of current mirrors. It consists of input register, deviation-voltage source, multiplexing-net, arbiter array and obfuscation circuit. After configuring deviation-voltage source by applying different input challenges, the PUF circuit updates keys without physically replacement, and it can generate multi-bit keys in a clock cycle. In SMIC 65 nm CMOS technology, the layout of 36 ports configurable PUF occupies 24.8 μm×77.4 μm with custom designing. Experimental results show that the PUF circuit possesses better statistical characteristic of uniqueness and randomness, and it has a high reliability of 97.4% with respect to temperature variation from ?40 °C to 125 °C, and supply voltage variation from 1.08 V to 1.32 V. It can be effectively used in information security field.

Chaotic system is an important research object in the field of data encryption based on the chaos. The logistic chaotic mapping is the simplest and efficient chaotic system and is usually used by many encryption methods based on the chaos, thus the security of Logistic mapping becomes an important research point. To deal with the issue of attractors and blank area of the presence of the Logistic sequence, an improved Logistic mapping based on the relationship between initial value and the fractal control parameters is proposed. The variables interval of chaotic mapping is reasonable subsection by using this relationship, so the chaos control parameter region can be expanded, and the onto mapping range is extended to the entire control parameter interval. The improved Logistic mapping makes the chaotic sequence distribution more uniform, and solves the problem of “stability window”and the blank area etc. Compared with the improved Logistic and piecewise chaotic Logistic, the experimental results show that the chaotic characteristics of sequence generated by the improved mapping is significantly strengthened, has more uniform distribution, and better random performance index. In addition, the improved Logistic mapping has low computational complexity and is prone to implement. The improved Logistic mapping has broad application prospects in the fields of spread spectrum communication and chaotic cipher.

A novel method for liver segmentation from abdominal CT volumes based on graph cuts and border marching is proposed. First, to exclude complex background and highlight liver region, liver intensity and appearance models are built according to the characteristics of a given CT volume. Then, the intensity and appearance models together with location information from neighbor segmented slice are effectively integrated into graph cuts cost computation to segment the CT volume initially and automatically. Finally, to solve the under-segmentation issue of liver vessel, a boundary compensation method based on border marching is proposed. The proposed method is tested and compared with some other methods on 30 CT volumes from XHCSU14 and SLIVER07 databases. The experimental results show that the proposed method can segment livers integrally and effectively from abdominal CT volumes, with higher accuracy and robustness.