Novel way to research nonlinear feedback shift register

In this paper,we regard the nonlinear feedback shift register（NLFSR） as a special Boolean network,and use semi-tensor product of matrices and matrix expression of logic to convert the dynamic equations of NLFSR into an equivalent algebraic equation. Based on them,we propose some novel and generalized techniques to study NLFSR. First,a general method is presented to solve an open problem of how to obtain the properties（the number of fixed points and the cycles with different lengths） of the state sequences produced by a given NLFSR,i.e.,the analysis of a given NLFSR. We then show how to construct all 2^2^n-（l-n）/2^2^n-lshortest n-stage feedback shift registers（nFSR） and at least 2^2^n-（l-n）-1/2^2^n-l-1shortest n-stage nonlinear feedback shift registers（nNLFSR） which can output a given nonperiodic/periodic sequence with length l. Besides,we propose two novel cycles joining algorithms for the construction of full-length nNLFSR. Finally,two algorithms are presented to construct 2^2^n-2-1different full-length nNLFSRs,respectively.     

New application methods for word-oriented cryptographic primitives

Modern software oriented symmetric ciphers have become a key feature in utilizing word-oriented cryptographic primitives.Using the output sequence,in the order of its generation,of a word-oriented cryptographic primitive in the same way as traditional bit-oriented primitives,we can expose the intrinsic weakness of these primitives,especially for word-oriented linear feedback shift registers,T-functions,and so on.Two new methods for using word-oriented cryptographic primitives are presented in this paper,that is,the extracted state method and cascading extracted coordinate method.Using a T-function as an example,we research the different cryptographic properties of the output sequences of the original method and the two proposed methods,focusing mainly on period,linear complexity,and k-error linear complexity.Our conclusions show that the proposed methods could enhance at low cost the cryptographic properties of the output sequence.As a result,since the new methods are simple and easy to implement,they could be used to design new word-oriented cryptographic primitives.     

Using Timed Petri Net to Model Instruction-Level Loop Scheduling with Resource Constraints

This paper uses timed petri net to model and analyze the problem of instructionlevel loop scheduling with resource constraints,which has been proven to be an NP complete problem.First,we present a new timed Petri net model to integrate functional unit allocation,register allocation and spilling into a unified theoretical framework.Then we develop a state subgraph,called Register Allocation Solution Graph,which can effectively describe the major behavior of our new model.the main property of this state subgraph is that the number of all its nodes is polynomial.Finally we present and prove that the optimum loop schedules can be found with polynomial computation complexity,for almost all practical loop programs.Our work lightens a new idea of finding the optimum loop schedules.     

Cost effective approach to identify multiple influential spreaders based on the cycle structure in networks

Identifying influential spreaders has theoretical and practical significance in complex networks.Traditional centrality methods can efficiently find a single spreader, but it could lead to influence redundancy and high initializing costs when used to identify a set of multiple spreaders. A cycle structure is one of the most crucial reasons for the complexity of a network and the cornerstone of the feedback effect. From this novel perspective, we propose a new method based on basic cycles in netw...     

The output feedback control for uncertain nonholonomic systems

This paper considers the problems of almost asymptotic stabilization and global asymptotic regulation （GAR） by output feedback for a class of uncertain nonholonomic systems. By combining the nonsmooth change of coordinates and output feedback domination design together, we construct a simple linear time-varying output feedback controller, which can universally stabilize a whole family of uncertain nonholonomic systems. The simulation demonstrates the effectiveness of the proposed controller.     

Adaptive contourlet-wavelet iterative shrinkage/thresholding for remote sensing image restoration

In this paper, we present an adaptive two-step contourlet-wavelet iterative shrinkage/thresholding （TcwlST） algorithm for remote sensing image restoration. This algorithm can be used to deal with various linear inverse problems （LIPs）, including image deconvolution and reconstruction. This algorithm is a new version of the famous two-step iterative shrinkage/thresholding （TWIST） algorithm. First, we use the split Bregrnan Rudin-Osher-Fatemi （ROF） model, based on a sparse dictionary, to decompose the image into cartoon and texture parts, which are represented by wavelet and contourlet, respectively. Second, we use an adaptive method to estimate the regularization parameter and the shrinkage threshold. Finally, we use a linear search method to find a step length and a fast method to accelerate convergence. Results show that our method can achieve a signal-to-noise ratio improvement （ISNR） for image restoration and high convergence speed.     

Fast correlation attack on streamcipher ABC v3

ABC v3 is a stream cipher submitted to the ECRYPT eStream project and has entered the second evaluation phase. Its key length is 128 bits. In this paper, we find large numbers of new weak keys of ABC family and introduce a method to search for them, and then apply a fast correlation attack to break ABC v3 with weak keys. We show that there are at least 2^103.71 new weak keys in ABC v3. Recovering the internal state of a weak key requires 236.05 keystream words and 2^50.56 operations. The attack can be applied to ABC vl and v2 with the same complexity as that of ABC v3. However, the number of weak keys of ABC vl as well as ABC v2 decreases to 2^97 ＋ 20^95.19,It reveals that ABC v3 incurs more weak keys than that of ABC vl and v2.     

A MIMO system with finite-bit feedback based on fixed constellations

In this paper,a MIMO system with finite-bit feedback based on fixed constellations is considered.Based on performance analysis of the system,an optimal operating system with maximum-likelihood decoding is demonstrated.Surprisingly,this operation reveals that the optimal way for the system to transmit signals needs to invoke the multimode scheme.We propose designing criteria for this scheme and methods for pre-codebooks,and a method to determine the number of modes for any specific channel.Furthermore,to reduce encoding complexity at the receiver side,we also develop a fast encoding algorithm.Theoretical analysis and simulations show that the proposed systems offer considerable gain over existing systems.Moreover,these systems also have much lower encoding complexity.Indeed,for the case of a MIMO system with two pairs of transmitting and receiving antennas,a properly designed system with a transmission rate of 8 bits per channel use and with 6-bit feedback can provide about a 1.5 dB performance gain over a beamforming system.     

Whitening of fractional order polynomial autoregressive Non-Gaussian impulsive stable colored noise

This paper proposes a new different spectral density from power spectrum density of second order processes, thus we can get a new concept of stable white noise based on covariation function and covariation spectrum density. And a new technique is suggested for the estimation of the parameters of polynomial autoregressive （PAR） nonlinear colored noise processes with stable white noise excitations. Simultaneously, a simple whitening method is employed for polynomial autoregressive nonlinear stable colored noise.     

H∞-Based Selective Inversion of Nonminimum-phase Systems for Feedback Controls

Stably inverting a dynamic system model is fundamental to subsequent servo designs. Current inversion techniques have provided effective model matching for feedforward controls.However, when the inverse models are to be implemented in feedback systems, additional considerations are demanded for assuring causality, robustness, and stability under closed-loop constraints. To bridge the gap between accurate model approximations and robust feedback performances, this paper provides a new treatment of unstable zeros in inverse design. We provide first an intuitive pole-zero-map-based inverse tuning to verify the basic principle of the unstable-zero treatment. From there, for general nonminimum-phase and unstable systems, we propose an optimal inversion algorithm that can attain model accuracy at the frequency regions of interest while constraining noise amplification elsewhere to guarantee system robustness. Along the way, we also provide a modern review of model inversion techniques. The proposed algorithm is validated on motion control systems and complex high-order systems.     

Finding slid pairs in trivium with MiniSat

A pair of distinct (key, IV) is said to be slidable if they can produce two identical keystreams up to a shift. In this paper, we investigate slid pairs in Trivium. We convert the resulting nonlinear sliding equations into a SAT instance and use MiniSat, a SAT solver, to solve it. We not only find more slid pairs than previous result on slid pair in Trivium but also provide a new method which can treat higher clock-shifts and is much less time-consuming.     

Retrospect and prospect on integration of gain and phase for feedback stability analysis

In this paper, we review existing approaches to integrating small gain and small phase analysis for feedback stability of dynamical systems, and give a brief outlook for possible future directions in exploring this topic. Small gain analysis has been very successful and popular in control theory since 1960s, while the small phase analysis for multiple-input-multiple-output systems has not been well understood until recently and is now gradually taking shape. Nevertheless, there have been attempts to analyzing feedback stability via the integration of gain and phase information over decades, including the combination of small gain with positive realness as well as that with negative imaginariness. Such combinations can be subsumed into a recently proposed framework for gain-phase integration, which brings in new geometrical methods and also sheds new lights on several future directions.     

Improved Collision Attack on Hash Function MD5

In this paper, we present a fast attack algorithm to find two-block collision of hash function MD5. The algorithm is based on the two-block collision differential path of MD5 that was presented by Wang et al. in the Conference EUROCRYPT 2005. We found that the derived conditions for the desired collision differential path were not sufficient to guarantee the path to hold and that some conditions could be modified to enlarge the collision set. By using technique of small range searching and omitting the computing steps to check the characteristics in the attack algorithm, we can speed up the attack of MD5 efficiently. Compared with the Advanced Message Modification technique presented by Wang et al., the small range searching technique can correct 4 more conditions for the first iteration differential and 3 more conditions for the second iteration differential, thus improving the probability and the complexity to find collisions. The whole attack on the MD5 can be accomplished within 5 hours using a PC with Pentium4 1.70GHz CPU.     

Approximation Algorithms for 3D Orthogonal Knapsack

We study non-overlapping axis-parallel packings of 3D boxes with profits into a dedicated bigger box where rotation is either forbidden or permitted, and we wish to maximize the total profit. Since this optimization problem is NP-hard, we focus on approximation algorithms. We obtain fast and simple algorithms for the non-rotational scenario with approximation ratios 9 ε and 8 ε , as well as an algorithm with approximation ratio 7 ε that uses more sophisticated techniques; these are the smallest approximation ratios known for this problem. Furthermore, we show how the used techniques can be adapted to the case where rotation by 90° either around the z-axis or around all axes is permitted, where we obtain algorithms with approximation ratios 6 ε and 5 ε , respectively. Finally our methods yield a 3D generalization of a packability criterion and a strip packing algorithm with absolute approximation ratio 29/4, improving the previously best known result of 45/4.     

生物网络通信语言的设计和实现

In our bio-network computation model ,it urgently needs a kind of network communication language to solve the communication and cooperation problems among bio-entities. Then the bio-entities can interact each other to implement emergent service and computation better. In this paper,we design a kind of bio-network communication language (BNCL)by combining the advantages of the Agent Communication Language (ACL)and XML. BNCL is simple, flexible and can implement the complicated coordination strategies in the bio-network. We examine the feasibility of BNCL by an application instance of the merchandise sales service with discount. BNCL also offers a new solution to interaction and cooperation problems of application programs in multi-agent systems and distributed systems.     

Resource allocation algorithm with limited feedback for multicast single frequency networks

The single frequency network (SFN) can provide a multimedia broadcast multicast service over a large coverage area. However, the application of SFN is still restricted by a large amount of feedback. Therefore, we propose a multicast resource allocation scheme based on limited feedback to maximize the total rate while guaranteeing the quality of service (QoS) requirement of real-time services. In this scheme, we design a user feedback control algorithm to effectively reduce feedback load. The algorithm determines to which base stations the users should report channel state information. We then formulate a joint subcarrier and power allocation issue and find that it has high complexity. Hence, we first distribute subcarriers under the assumption of equal power and develop a proportional allocation strategy to achieve a tradeoff between fairness and QoS. Next, an iterative water-filling power allocation is proposed to fully utilize the limited power. To further decrease complexity, a power iterative scheme is introduced. Simulation results show that the proposed scheme significantly improves system performance while reducing 68% of the feedback overhead. In addition, the power iterative strategy is suitable in practice due to low complexity.     

Feature selection on probabilistic symbolic objects

In data analysis tasks, we are often confronted to very high dimensional data. Based on the purpose of a data analysis study, feature selection will find and select the relevant subset of features from the original features. Many feature selection algorithms have been proposed in classical data analysis, but very few in symbolic data analysis （SDA） which is an extension of the classical data analysis, since it uses rich objects instead to simple matrices. A symbolic object, compared to the data used in classical data analysis can describe not only individuals, but also most of the time a cluster of individuals. In this paper we present an unsupervised feature selection algorithm on probabilistic symbolic objects （PSOs）, with the purpose of discrimination. A PSO is a symbolic object that describes a cluster of individuals by modal variables using relative frequency distribution associated with each value. This paper presents new dissimilarity measures between PSOs, which are used as feature selection criteria, and explains how to reduce the complexity of the algorithm by using the discrimination matrix.     

Distributed optimal consensus of multiple double integrators under bounded velocity and acceleration

This paper studies a distributed optimal consensus problem for multiple double integrators under bounded velocity and acceleration. Assigned with an individual and private convex cost which is dependent on the position, each agent needs to achieve consensus at the optimum of the aggregate cost under bounded velocity and acceleration. Based on relative positions and velocities to neighbor agents, we design a distributed control law by including the integration feedback of position and velocity errors. By employing quadratic Lyapunov functions, we solve the optimal consensus problem of double-integrators when the fixed topology is strongly connected and weight-balanced. Furthermore, if an initial estimate of the optimum can be known, then control gains can be properly selected to achieve an exponentially fast convergence under bounded velocity and acceleration. The result still holds when the relative velocity is not available, and we also discuss an extension for heterogeneous Euler-Lagrange systems by inverse dynamics control. A numeric example is provided to illustrate the result.     

Output feedback stabilization of stochastic high-order planar nonlinear systems with stochastic inverse dynamics and output-constraint

In this paper, we solve the output feedback control problem of stochastic high-order planar nonlinear systems with output constraint and stochastic integral input-to-state stability(SiISS) inverse dynamics. By employing a key coordinate transformation, a stochastic nonlinear system with output constraint and SiISS inverse dynamics is converted into an unconstrained system. By skillfully constructing an observer and adopting SiISS small-gain conditions, we develop a new output feedback control de...     

Design of robotic visual servo control based on neural network and genetic algorithm

A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation (BP) neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP algorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.