This paper presents a novel and robust point pattern matching algorithm in which the invariant feature and the method of spectral matching are combined. A new point-set based invariant feature, Relative Shape Context (RSC), is proposed firstly. Using the test statistic of relative shape context descriptor’s matching scores as the foundation of new compatibility measurement, the assignment graph and the affinity matrix of assignment graph are constructed based on the gained compatibility measurement. Finally, the correct matching results are recovered by using the principal eigenvector of affinity matrix of assignment graph and imposing the mapping constraints required by the overall correspondence mapping. Experiments on both synthetic point-sets and on real world data show that the proposed algorithm is effective and robust.

Auto-classification of online goods is a great need for intelligent e-commerce. Some valuable information of product, such as long sleeve T-shirt vs short one,round collar vs V-neck collar, can be tagged based on the image features and classification algorithms. As two complementary features,PHOG (Pyramid Histogram of Orientated Gradients) and PHOW (Pyramid Histogram Of Words) are adopted to extract and describe the features of product-images. An improved nearest-neighbor classifier based on the class descriptor of images is proposed. Experimental results show that the accuracies have been achieved between 70% to 99% on 2 or 3 real-time classification task, which is markedly improved compare to the existent result.

The standard Compressed Sensing (CS) reconstructions of image exploit simply the sparse priors of the wavelet coefficients, ignoring the structural information of the wavelet coefficients. In this paper, the Hidden Markov Tree ( HMT ) model is integrated in the compressed sensing，which has been found successful in capturing the key features of the joint probability density of the wavelet coefficients of real-world image. An optimization issue which is similar to the standard compressed sensing is derived from the MAP reconstructions for the image based on HMT model, and an alternating convex projection algorithm based on Bayesian optimization is proposed. What’s more, a universal HMT (uHMT) model based on the dual-tree wavelet transform and its improved form are integrated to improve the reconstruction performance further, instead of the HMT model of the orthogonal wavelet transform. As the experiments show, the average Peak Signal-to-Noise Ratio (PSNR) of the reconstructed image based on the improved uHMT (iuHMT) model in the dual-tree wavelets domain outperforms uHMT model 0.97 dB.

A novel 3D face intrinsic attributes descriptor is proposed in this paper. The presented descriptor is used to solve the feature inhomogeneous distribution and redundancy for high resolution 3D face model. In presented method, an improved Quadric Error Metrics (QEM) mesh decimation method is first developed based on semi-edge folding and adaptive cost weighting. Multi-resolution 2D intrinsic attributed image can be then obtained by homeomorphically mapping 3D decimated facial mesh into 2D plane with the highest attribute preserving based on feature restricted conformal isomorphic mapping. Consequently, 3D surface matching issue can be simplified to a 2D image matching issue by comparing the resulting 2D intrinsic attributed images, which are stable and robust to occlusion and noise. Experimental results on GavabDB show that presented method has the ability to represent intrinsic information of 3D face and achieve significant improvements on recognition accuracy compared with baselines.

Surface is one of the important features in industrial CT (ICT) cubic data. In actual, most of the surface features do not exist alone, but exist in the form of different gray regions. 3D-Wedgelet, one of the multiscale geometric analysis methods, provides a powerful competence to describe this structure. After analyzing the component and relation of Planelet in mono-scale, a fast 3D-Wedgelet decomposition method is gained. On the basis of it, two ICT cubic data’s surface feature extraction methods are proposed. The first one is based on multiscale 3D-Wedgelet decomposition. The second one is composed of several parts as follows, first dividing the ICT cubic data into 3 slice group in 3 perpendicular directions each other, then extracting linear features based on multiscale Wedgelet decomposition, finally fusing the 3 slice group’s linear features to gain surface features. Numerical experimental results show that the methods can extract the surface features of ICT cubic data effectively.

A novel algorithm for robust adaptive beamforming is presented in this paper to solve the problem of the mismatch errors between the actual and presumed steering vectors. By using the orthogonal component of the errors, the method can obtain the desired steering vectors successfully. Two orthogonal subspaces are constructed firstly, and then the mismatch errors are orthogonally decomposed, so the correcting process of steering vectors is transformed into an iterative quadratic convex optimization issue by using a slack variable. The proposed algorithm does not need the conditions of the norm or probability constraint of the mismatch errors. Therefore, it avoids estimating upper bound values and probability distribution modeling. Simulation results show the effectiveness of the proposed algorithm.

Personalization and accuracy are the key issues for the development information filtering. Research on semantics understanding will help solve the issues. The basic idea is to describe the semantics of information content and user requirements formally so that computer would understand the formal semantics. Then the semantics would be criterion for information filtering. In this paper information domain ontology is defined to describe semantics. Information semantics is composed by information feature terms and their explanations, and user requirements semantics is represented by definite requirements and latent requirements. Furthermore, the judging methods of information semantics understanding and user requirement semantic understanding are proposed. Finally, this paper presents the information filtering algorithm based on semantic understanding. Experiments show that the information filtering algorithm is effective and feasible.

In consideration of the measurement data association problem of multiple passive sensors, a novel data association algorithm based on the cotangent of angles is proposed, which improves the computing efficiency by dealing with the angle information directly, thus the conversion from angles to the distances of dissenting beelines is avoidable. Firstly, all possible associations are screened by azimuth angle test and elevation angle test, then the indicated function analysis is used to select the correct association from the set of candidates. The simulation results show that the proposed algorithm is much faster than the conventional ones without the decrease of performance.

This paper analyzes the character of moving targets, studies deeply the relationship of Doppler parameters and target shift. A single-channel SAR-GMTI method based on target defocusing shifted difference is proposed. This method employs the character to separate moving targets from stationary. The character is that the azimuth mismatch filter performs compression, it induces shifted difference between the stationary and moving targets because of having different Doppler center. Compare with traditional methods, it is applicable to low and high squint case and the detection ability is significantly improved.

Resolving multiple targets in one beam is important to low-resolution radar. A method to resolving multiple targets is proposed using time-frequency transform based on the defference of targets micro-doppler. Adaptive chirplet transform is used to remove across item of signals, the main frequency of each target is got using adaptive chirplet transform, since the main frequency is linear, hough transform is used to detect the numbers of lines, then the numbers of multiple targets in one beam is got. Simulation experiments show that this method can effectively estimate the numbers of multiple targets.

Due to the range-Doppler coupling effect of the LFM signal, the micro-Doppler caused by the micro- motion targets will also bring the bias detection range. Take the even rotational speed target as an example, the change of this bias detection range with time obeys the sinusoid, and this bias is large enough for the narrowband tracking radar. A new detection and estimation algorithm for the character of micro-motion based on the narrowband tracking information is proposed in this paper. The simulation result and the dynamic measure date of precession target in microwave anechoic chamber, which are designed according to the typical parameters of the tracking radar for the special targets, validate the conclusion of this paper and the effectiveness of detection and estimation algorithm.

The radar echo separation of appendix part is the key for improving ISAR image of ballistic target. This paper establishes the echo model of ballistic target. And the Doppler difference of body and appendix part is analyzed. A new decomposition in slow-time domain is proposed. The body echo is decomposed into several LFM signal components. The echo of body and appendix is reconstructed. Explicit procedure is given. Simulation result shows that the ISAR image is much better compared to the image based on original data.

Compared with the monostatic radar, micro-Doppler effect in bistatic radar has many differences due to its spatial complexity. In this paper, micro-Doppler effect of target with rotating parts is derived in the bistatic ISAR system. Characteristics and manifestation of micro-motion part in the range–slow-time plane is also analyzed. According to the effect produced by the system’s spatial relationship, a modified extended Hough transform is proposed to extract the micro-motion information from the range–slow-time plane. Simulations verify the correctness and effectiveness of the method in bistatic ISAR system.

Ground moving target indication for multiple carrier-frequencies spaceborne MIMO-SAR system is explored in this paper. According to the characteristics of multiple carrier-frequencies spaceborne MIMO-SAR, Space Frequency Adaptive Processing (SFAP) algorithm based on eigenspace projection is employed for suppressing clutter, and then the Double-Threshold Constant False Alarm Rate (DT-CFAR) is proposed for detecting moving targets. DT-CFAR, firstly, uses TM-CFAR to detect moving targets after the clutter corresponding to different carrier-frequencies is suppressed by SFAP, and then the detected result is through accumulating multiple pulses detector to improve detection probability and decrease false probability. Simulation results prove, farther, the validity and great performance of multiple carrier-frequencies spaceborne MIMO-SAR system for ground moving target indication.

Scattering interference will result in large regions being blurred while forming a SAR image, and Space Fast-time Adaptive Processing (Fast time STAP) can be utilized to mitigate its influence. A scattering interference mitigation method based on Cross Spectral Metric (CSM) for Multi-channel SAR (MSAR) is presented in this paper, and the cross spectrum of the interference plus noise covariance matrix is analyzed. Also, the interference mitigation performance of the new method is analyzed, and is compared with the performance of the method based on Principal Component Analysis (PCA). The simulation results demonstrate that CSM performs better than PCA in scattering interference mitigation.

In this paper, a simulation model of shallow sea topography imaging by Along-Track Interferometric SAR (ATI-SAR) is developed based on shallow sea current dynamical model, wave-current interaction model, backscattering model and Doppler spectrum model. This simulation model is used to study the influence of radar parameters (frequency, incidence angle, baseline length and polarization) on shallow sea topography imaging by ATI-SAR under quasi-one-dimension condition. The simulation results indicate that high frequency, large incidence angle and long along-track baseline are favorable to shallow sea topography imaging by ATI-SAR when the imaging time lag between two antennas is shorter than the de-correlation time of the sea. Considering the influence of Signal-to-Noise Ratio (SNR), VV is a favorable polarization for shallow sea topography imaging, though the ATI phase may be not sensitive to the polarization.

This paper dedicates to propose a supervised local decision Hierachical Support Vector Machine (HSVM) learning model for anomaly intrusion detection in high dimensional feature space. The binary-tree structure of HSVM presents a “divide-and-conquer” algorithm for complex anomaly intrusion detection problem, i.e., the training signal for supervising local decision at each internal node is constructed according to information gain criterion. The embedded SVMs at internal node are trained on local optimized feature subsets standing on the sensitivity degrees of a margin to features. The experimental results suggest that the proposed anomaly intrusion detection method can gain learning model with better stability under the local decision supervisal of training signals. Further, it also achieves competitive detection accuracy and higher detection efficiency with condensed feature information.

There are two focuses in biometric applications, one is the traditional authentication and identification technology, and another is the key generation technology. The character code extraction from iris information, the random string extraction from character code using fuzzy method and the embedding of random string in the elliptic curve are studied. A new digital identity signature scheme based on iris information is proposed, which takes the iris information as the public key. Using two independent PKGs, the proposed scheme could avoid the signature forgeability of the single PKG, hence it has more security and reliability. With the random oracle model, the proposed scheme is proved to have the unforgeability secure when adaptively choose plaintext and identity attack, and it can  effectively resist the birthday attack.

Most available identity-based authenticated key agreement protocols require that both protocol participants are from the same PKG(Private Key Generator) environment. However, safe communication is requisite between protocol participants that are from different PKG environment in practice. In this paper, a new identity-based AKA (Authenticated Key Agreement) protocol is proposed in multiple PKG environment, which makes use of a new technique called twin Diffie-Hellman problem. Moreover, the security of the proposed protocol can be reduced to the standard CDH and BDH assumption in the eCK model, which makes use of the “trapdoor test” technology proposed by Cash et al in Eurocrypt 2008.

Under the hard assumption of SIS (Short Integral Solution), a lattice-based ring signature scheme in bonsai tree model is proposed, which based on the bonsai tree signature scheme. Security of proposed ring signature is based on the hardness of SIS. The privacy of signer is guaranteed in proposed ring signature. This ring signature is also unforgeability, which is proved in the standard model (without random oracle).

Network anomaly detection is critical to guarantee stabilized and effective network operation. Although PCA-based network-wide anomaly detection algorithm has good detection performance, it can not satisfy demands of online detection. In order to solve the problem, the traffic matrix model is introduced and a Multivariate Online Anomaly Detection Algorithm based on Singular Value Decomposition Updating named MOADA-SVDU is proposed. The algorithm constructs normal subspace and abnormal subspace incrementally and implements online detection of network traffic anomalies. Theoretic analysis shows that MOADA-SVDU has lower storage and less computing overhead compared with PCA. Analyses for traffic matrix datasets from Internet and simulation experiments show that MOADA-SVDU algorithm not only achieves online detection of network anomaly but also has very good detection performance.

A tracking algorithm based on probability density propagation which can deal with non-linear and non-Gaussian issues is proposed. The Gaussian mixture model is adopted to represent the prior density distribution, posterior density distribution and likelihood distribution. The unscented transformation is used to deal with the non-linear prediction and approximation method is used to achieve the posterior density distribution. Finally, the weighted centroid point of the posterior density distribution’s different modes is calculated and set as the current position of the target. Simulation results indicate that the proposed algorithm can deal with the tracking task in wireless sensor network under strong noise.

Traffic balancing in routing optimization targets to minimize network congestion in traffic engineering. How to select paths for traffic balancing becomes a challenging problem. For minimizing network congestion, this paper argues that network congestion is determined by paths’ congestion, and proposes the minimizing sum of path-cost model of traffic balancing. For path selection in routing traffic, a minimal cost path algorithm is proposed. On the basis of real network topology and traffic demand, experiments are conducted to verify the model and algorithm proposed, and results show a nearly 20% decrease of maximal link utilization when network is becoming congested.

This paper focuses on layer rate allocation in network coding based layered media multicast. The sum of receiving rates of all receivers is maximized by optimizing layer rate allocation. Since it is a nonlinear integer programming, a heuristic algorithm, MRAA (M-layer Rate Allocation Algorithm), is proposed with the time complexity O(|V||T|²|E|²)  to solve it. Algorithm MRAA decomposes the network graph into several subgraphs, and the minimum of the maximum flows of all receivers in each subgraph is the corresponding layer rate. Numerical results indicate that the algorithm MRAA can increase the network throughput by 15%-18% and improve the average bandwidth utilization rate of the receivers by 6%-10%, compared with the algorithm in the existing work.

Targets tracking is one of the key applications of wireless sensor networks. In this kind of applications, the sensing process on the targets’ moving paths, rather than in the whole area, is often of more concern. The path 1-coverage problem has been extensively investigated, however, the path k-coverage (k>1) problem receives little attention. To address this issue, in this paper, the coverage problem induced on an intrusion path by a randomly deployed two-dimensional sensor network is first transformed into a one-dimensional coverage problem, and then a theoretical lower bound to the full k-coverage probability of a randomly selected straight path is derived. Experimental results show that this lower bound is very close to the simulation results, especially when k is small.

This paper presents a broadcast algorithm called Space-Covered Broadcast (SCB) that does not need and neighbor information to address broadcast storm problem in multi-hop wireless networks. The SCB algorithm uses the minimum number of forwarding nodes to cover the network by optimizing the spatial distribution of the forwarding nodes, thereby it reduces retransmissions considerably while providing high deliverability. The SCB algorithm reduces the communication and memory overhead for it does not need any neighbor information or network topology awareness, and the algorithm adapts itself to and gets the best out of existing channel conditions for the forwarding nodes are entirely self-selected at each receiving node using a delayed retransmitting mechanism. Simulation results show that SCB can significantly improve broadcast efficiency for different network density and network load.

Customers can not get high QoS from the streaming service systems with VCR operation because of the random requests. This paper derives the expectation of the demanding delay with the methods of prefetching and segment-based caching. A near-optimal policy of cache management is given, and the solution can approximate to the optimal one by computing online. The prefetching algorithm is given in the paper. The algorithm can reduce the delay of demands from clients with cooperation of caching and prefetching for improving the efficiency of cache. Simulation results show the effectiveness of proposed algorithm.

In order to minimizing the total transmit power while guaranteeing the outage performance at the same time, in this paper a distributed power allocation and relay selection scheme is proposed for amplify-and-forward cooperative networks with multiple sources and multiple relays. In the proposed scheme, sources select their relays independently, and introduce channel-related timers to compete for their preferred relay. Then the selected relays independently determine whether to forward the messages or not according to a forwarding threshold. Simulation results show that the proposed scheme can reduce the total transmit power of the network significantly and has a similar performance to the optimal central-controlled scheme with a much lower overhead.

This paper presents an intact scheme of timing synchronization for the Multi-Band OFDM (MB-OFDM) Ultra Wide Band (UWB) system adopted by IEEE 802.15.3a proposal and ECMA-368 standard. The scheme is determined through algorithm improvement and simulation. Frame detection and coarse timing are implemented with the Maximum Correlation (MC) method based on the first-band information of the preamble. Fine timing is achieved with the minimum energy ratio method based on the information of all the three bands. Besides, revision of the timing position is performed twice that performance of the algorithm is guaranteed. Simulations show that residual timing error can be kept at a small scale which can be compensated with channel in frequency domain. And else, the scheme has a low complexity.

According to the actual spectrum demand of cognitive radio, a new spectrum-hole detection algorithm, which bases on triple matrix accumulation, is presented for?cognitive?radio?systems?for?the?first?time by the study on weak signal detection technology. The algorithm combines spectrum-domain block adaptive filtering with matrix reshape, accumulate products estimation and frequency-domain smoothing to realize the weak signal detection. Finally, the performance of the algorithm is analyzed and?simulated?by?using?a?QPSK?signal. The result shows that the detection technology can detect weak signal quickly and effectively, and has a higher detection probability. The detection technology may be applied wholly to cognitive radio field.

The cooperative communication can get better performance of systems than the direct transmission. In particular, how to manage cooperative relays and allocate power among them especially for Cognitive Radio (CR) is one of the most important issue in wireless cooperative diversity systems. For CR systems, cooperative power allocation strategy need consider avoiding the interference with the primary (licensed) users. In this paper, a distributed transmit power allocation algorithm for multi-relays based on Amplify and Forward (AF) cooperative transmission is proposed for spectrum sharing CR systems. It optimizes the performance of the CR system, while the interference experienced by the licensed users is limited. The simulations validate the effectiveness of proposed algorithm by comparing it with the direct transmission and the scheme where equal weights are assigned to the relays.

For the chip rate sampled channel impulse response of the IR-UWB system, a novel optimum data-aided channel estimation scheme is proposed that utilizes multi-templates to extract the frame-level samples from the received signal. Each frame-level rate sample for any given template is decomposed to a sum of a composite channel parameter (i.e., a linear combination of some channel parameters) and a noise. Based on these frame-level rate data and the maximum likelihood (ML) criterion, the ML estimates can be obtained for the composite channel parameters contained in these samples. The channel parameters can be computed through solving a set of the linear equations determined by the composite channel parameters. The simulations are provided to demonstrate the effectiveness of the proposed approach.

A novel Narrow-Band Interference (NBI) suppression method of IIR lattice notch filter based on the combination of NBI suppression in both time and frequency domain in DSSS is proposed. This method is implemented in a parallel processing structure. The frequency of NBI signal is accurately obtained by employing optimal window functions in the time domain and spectrum correcting method in frequency domain. The optimal rejection bandwidth of IIR notch filter in the frequency domain is obtained adaptively according to the error of frequency estimation and interference to signal ratio. Then the IIR notch filter coefficients are adjusted timely and adaptively. Analysis and results of the experiment show that the proposed method has better stabilization and real-time property than traditional adaptive NBI suppression methods of IIR, and minimizes the distortion of the signal in the full suppression of narrow-band interference.

To study EM scattering of a Perfectly Electric Conducting (PEC) target below the layered rough surface, a fast cross coupling iterative approach based on Bi-Conjugate Gradient (Bi-CG) method and Forward Backward Method (FBM) is presented in this paper. At first, the Electric Field Integral Equations (EFIE) of the induced currents on the rough surface and the target are derived. An iterative approach is developed to solve the two EFIE and scattering from both the target and underlying surface. The EFIE of the rough surface and the target are solved by using FBM and Bi-CG respectively. The mutual effect between the rough surface and the target are finished by iterative Calculation. The EM scattering from a metallic cylinder below the layered rough surface with two Gauss rough surfaces is computed. The result shows that the scattering coefficient is agreement with that of layered rough surfaces when the target is small enough. The relationship between the scattering pattern and the target’s size and station are also discussed. Results show a buried PEC cylinder can significantly alter the scattering behaviors of layered rough surfaces.

Based on transformation-optical approach, the distribution of permittivity and permeability for the N-sided regular polygonal electromagnetic wave concentrator is derived and functionality of the concentrator is numerically confirmed by the finite element solver COMSOL. Electric field and total energy distributions in the vicinity of the three-, four-, five- and six-sided regular polygonal concentrators are simulated. The influence of the focus area and deviation of material parameters from perfect N-sided regular polygonal concentrator on the concentrating effect is studied. The results show that the smaller the focus area, the stronger the total energy density; The deviation of material parameters from perfect concentrator results in a distortion of the electrical field and total energy density distribution.

A 2.5-Dimensional (2.5D) Particle-In-Cell (PIC) model is developed to study complicated nonlinear beam-wave interaction in the high-power klystron. Firstly, the particle charge and the beam current are properly assigned onto the grid based on the concept of PIC simulation. Secondly, the Finite-Difference-Time-Domain (FDTD) method is introduced to solve Maxwell’s equations so that the space charge of the electron beam can be accurately described. Thirdly, the port-approximation method is employed to simulate high-frequency cavity fields. Finally, the Lorentz equation is solved to further advance particle motion. The FDTD method and the port-approximation idea are combined for the first time in this model, which is more accurate than the traditional 1D nonlinear model, and, at the same time, is more efficient than the full 3D PIC method. The first domestic 2.5D particle-in-cell code, KLY2D, is developed based on this model. And an S-band 50MW high peak-power klystron is simulated using this code. The consistency between the theoretical results and the experimental results indicates the reliability of the theoretical model and the simulation code, which is of importance for further promoting the design and the development of high power klystrons.

For the detection problem caused by point targets broken up into distributed targets in the high-range resolution radar system, an adaptive detection algorithm based on the Rao test is proposed. The distributed target, which is modeled as a subspace random signal, may be expanded both in range and also in Doppler frequency axes. The unknown parameters are estimated by maximum likelihood estimation only under hypothesis H₀ in Rao test. Moreover, the detector is obtained relying on the two-step design procedure. Because of these, both the computational burden and the complexity are cut down. Finally, performances of the proposed detector are assessed through Monte Carlo simulations and are shown to have better detection performance of distributed targets in K-distributed clutter compared with the existing similar detector.

Technique of Space-Time Adaptive Processing (STAP), which is usually employed by airborne radar to detect the ground moving targets, can not estimate the spatial-temporal parameters of the corresponding targets. Based on the linearly constrained adaptive monopulse technique, a new method for estimating the spatial-temporal parameters of moving targets is proposed in this paper. The proposed method utilizes the beam null, monopulse slope, and decoupling constraints to remove the worse influence on the beam shape from the mainbeam clutter, keep the monopulse characteristic, increase the estimation performance of the spatial-temporal parameters of the moving target. Simulation results illustrate that the proposed method possesses the excellent estimation performance of the spatial-temporal parameters compared with the other similar methods.

Temporal correlation matrices of the convolutive-mixture signals possess the struture of tri-factor multiplication and so can be jointly diagonalized blockwise. Though cutting to pieces and then reprogramming of the 3-D matrix composed of a group of correlation matrices, an alternating and iterative approach is proposed to achieve the least-squares solutions and then to estimate the channel mixture matirx for realizing convolutive blind separation in time domain. Compared with traditional joint block-diagonalization algorithms, simulation results show that the proposed one has better and more stable separation performance irrespective of initial parameters. The dissimilarity index and Bark sepectral distortion are improved by 4.35 dB and 0.22 respectively.

An efficient algorithm for multiple coherent sources’ two-dimensional Direction Of Arrival (DOA) estimation with a sparse uniform rectangular array of electromagnetic vector sensor (EmVS) is proposed. The algorithm can improve the DOA estimation performance by increasing the intervector sensor spacing to achieve aperture extension. The Polarization Smoothing Algorithm (PSA) is coupled with the propagator method (PM) to acquire cyclically ambiguous DOA estimates. In order to disambiguate the cyclic phase ambiguities, a novel pre-processing method is derived by Covariance Matrix Averaging (CMA) and identify the true DOA estimates from a set of cyclically ambiguous candidate estimates based on the vector sensor steering vector’s characteristics. Comparing with the existing extended aperture-based direction finding method, the proposed algorithm can estimate the DOA of coherent sources, and requires no eigen-decomposition, hence, has a lower computational complexity. Monte-Carlo simulations are presented to verify the effectiveness of the proposed algorithm.

3D cipher is a new block cipher proposed in CANS2008. It is different from all known block cipher as it uses the three dimension structure. The designers give out a 5-round impossible differential and make an impossible differential attack on 6-round 3D cipher. In this paper, some new 6-round impossible differentials are found according to its structure properties. Based on these new impossible differentials and the equivalent structure of 3D cipher, effective impossible differential attacks can be made on 7 and 8-round 3D cipher. Moreover, according to some properties of the key schedule, these attacks can be extended to 9-round 3D cipher. These attack results are better than the designer’s.

This paper studies the lower bounds on the second order nonlinearity of bent functions and semi-bent functions f(x,y) with n+1 variables, where x∈GF(2ⁿ), y∈GF(2). Firstly, the values of the nonlinearity of the 2ⁿ-1 derivatives of the Boolean function f(x,y) are given. Then, the tight lower bounds on the other 2ⁿ derivatives of  f(x,y) are deduced. Furthermore, the tight lower bounds on the second order nonlinearity of f(x,y) are presented. The derived bounds are better than the existing general ones. The results show that these functions f(x,y) have higher second order nonlinearity, and can resist the quardratic and affine approximation attacks.

Differential encoding used in conjunction with sequence decoding is a primary method to implement high performance noncoherent demodulation. Though noncoherent decoding based on SCCC with differential encoding is almost similar to coherent demodulation in performance, its high decoding complexity makes its engineering application difficult because of the introduction of modified zero-order Bessel function to estimate the channel transition probability. To solve this problem, two simplified noncoherent MAP decoding algorithms based on SCCC structure are proposed in this paper. These algorithms use an approximate formula of zero-order Bessel function to simplify the branch metrics in log-MAP algorithm of inner decoder. Theoretical analysis and simulation results show that the proposed algorithms can largely reduce the decoding computational complexity and have good performance.

A new algorithm named QGCC (Quasi-Grid based Cooperative Coverage) is proposed in this paper, which employs virtual quasi-grid structure and cooperative sensing model to reduce the number of active nodes, and to prolong the coverage lifetime. In QGCC, a low-complexity distributed method is designed to determine the vertexes of grid, and an energy efficient node scheduling scheme is presented. QGCC also introduces a new metric named irregularity to analyze the impact of the deviation between keeper and its ideal vertex. Simulations show that, compared to the reference algorithms, QGCC can efficiently reduce the number of active nodes, and prolong the coverage lifetime. Another notable feature of QGCC is that, the coverage lifetime is approximate linear related to the number of nodes, which means this algorithm can make good use of redundant nodes.

The transmission power control algorithm D-FPAV(Distributed Fair Power Adjustment for Vehicular environments) becomes computational expensive when required calculation precision is high, i.e., the step λ is small. In this paper, a novel transmission power control algorithm SDPC(Steepest Descent Power Control) algorithm is proposed to improve D-FPAV. This algorithm is based on the rapid convergence theory of the steepest descent method. Simulation results show that the effectiveness of SDPC in terms of the number of iterations for computing the transmission power values decreases and converging speed. As a result, the transmission power to transmit their status information can be quickly adaptive to the vehicle dynamic and surroundings situations. Therefore, channel congestion can be effectively controlled and the security of vehicular networks can also be improved.