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This paper addresses the problem of Concurrent Mapping and Localization(CML) by means of a hybrid immune evolutionary computation based on immunity and clonal selection for a mobile robot. An immune operator, a vaccination operator, is designed in the algorithm. The experiment results of a real mobile robot show that the computational expensiveness of the algorithm in this paper is less than other algorithms and the maps obtained are very accurate. [ABSTRACT FROM AUTHOR]

Conventionally the boilers in space heating systems are controlled by open-loop control systems due to the absence of a practical method for measuring the overall thermal comfort level in the building. This paper describes a neural-fuzzy based inferential sensor that can be used to design close-loop boiler control schemes. Both simulation and experimental results show that the proposed technique results in significant energy saving and improvement on the control of thermal comfort in the built environment. The paper also describes the ongoing and future work. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for visual inspection of textures. The approach applies the artificial immune theory to learning the filters for texture flaw detection, which are invariant to changes of texture orientations and scales. In this paper, defect textures and defect-free textures are regarded as non-self and self respectively, and texture filters are regarded as antibodies. The clonal selection based algorithm is presented to evolve antibodies. Experimental results on TILDA textile images were done to show the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Most existing spreading models for network viruses are developed refereing to the epidemic models for biological viruses. However, Why most network viruses spread much slower than those models predicate? Why most network viruses still exist when they go beyond the threshold predicated by those models? Contrary to the prior models, the paper points out network viruses have different spreading features compared with biological viruses, such as the connectivity rate and cure rate are both functions of the time which are also key factors to affect the spreading of viruses. Based on which the paper constructs a more general epidemiological model for the network viruses. For several particular cases the paper presents the simulations of the connectivity rate and cure rate and find they are consistent well with the statistics of some real viruses. Thus the paper opens one path to modifying the traditional epidemic models. [ABSTRACT FROM AUTHOR]

This paper presents the rule generation method based on evolutionary computation and rough set, which integrates the procedure of discretization and reduction using information entropy-based uncertainty measures and evolutionary computation. Based on the definitions of certain rules and approximate certain rules, the paper focuses on the reduction by meanings of evolutionary computation. Experimental results reveal that the proposed method leads to better classification quality and smaller number of decision rules comparing with other methods. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to introduce a recently-developed nature-inspired algorithm, harmony search (HS), and to apply the algorithm to water pump switching problem. The HS algorithm is conceptualized using the musical improvisation process of searching for a better state of harmony. This paper describes a HS algorithm-based approach for the optimal switching problem in serial water pumping system. A standard example from the literature is presented to demonstrate the effectiveness of the proposed method, and the results are compared to genetic algorithm and branch & bound method. Computational results indicate that the HS approach becomes a good optimization model for solving water pump switching problem. [ABSTRACT FROM AUTHOR]

This paper is mainly about methodology in designing quantum algorithm. Based on study of Grover's algorithm, we argue that it is a short cut to design and interpret quantum algorithms from the viewpoint of Householder transformation directly. We give an example for this claim, which extends Grover's quantum search algorithm to some structured database. In this example, we show how to exploit some special structure information of problem, which restricts the search in some subspace. Based on an instantiation of this framework, we show that it does can utilize the information to the full extent. This paper gives the details that produce the algorithm framework. The idea, which is simple and intelligible, is universal to some extent, and therefore can be applied to other similar situations. [ABSTRACT FROM AUTHOR]

Recently, many genetic algorithms (GAs) have been developed as an approximate method to generate Pareto frontier (the set of Pareto optimal solutions) to multi-objective optimization problem. In multi-objective GAs, there are two important problems : how to assign a fitness for each individual, and how to make the diversified individuals. In order to overcome those problems, this paper suggests a new multi-objective GA using generalized data envelopment analysis (GDEA). Through numerical examples, the paper shows that the proposed method using GDEA can generate well-distributed as well as well-approximated Pareto frontiers with less number of function evaluations. [ABSTRACT FROM AUTHOR]

The paper proposed a novel quantum-inspired genetic algorithm with only one chromosome, which we called Single-Chromosome Quantum Genetic algorithm (SCQGA). In SCQGA, by bringing the information representation in quantum computing into the algorithm, only one quantum chromosome (QC) is used to represent all possible states of the entire population. A novel quantum evolution method without using conventional genetic operators such as crossover operator and mutation operator is proposed, in which according to the best individuals generated by QC we adjust the quantum probability amplitude with quantum rotation gates so that the QC can produce more promising individuals with higher probability in the next generation. The paper indicated that SCQGA is a new approach belonging to estimation of distribution algorithms (EDAs). Experiments on solving a class of combinatorial optimization problems show that SCQGA performs better than conventional genetic algorithm. [ABSTRACT FROM AUTHOR]

This paper considers how to increase the capacities of the elements in a set E efficiently so that probability of the total cost for the increment of capacity can be under an upper limit to maximum extent while the final expansion capacity of a given family F of subsets of E is with a given limit bound. The paper supposes the cost w is a stochastic variable according to some distribution. Network bottleneck capacity expansion problem with stochastic cost is originally formulated as Dependent-chance programming model according to some criteria. For solving the stochastic model efficiently, network bottleneck capacity algorithm, stochastic simulation, neural network(NN) and genetic algorithm(GA) are integrated to produce a hybrid intelligent algorithm. Finally a numerical example is presented. [ABSTRACT FROM AUTHOR]

This paper presents a novel genetic algorithm (GA) for the scheduling problem of input-Queued switch, which can be applied in various networks besides the design of high speed routers. The scheduler should satisfy quality of service (QoS) constraints such as throughput and jitter. Solving the scheduling problem for the input-Queued switches can be divided into two steps: Firstly, decomposing the given rate matrix into a sum of permutation matrices with their corresponding weights; secondly, allocating the permutation matrices in one scheduling period based on their weights. It has been proved that scheduling problem in input-Queued switch with throughput and jitter constraints is NP-complete. The main contribution of this paper is a GA based algorithm to solve this NP-complete problem. We devise chromosome codes, fitness function, crossover and mutation operations for this specific problem. Experimental results show that our GA provides better performances in terms of throughput and jitter than a greedy heuristic. [ABSTRACT FROM AUTHOR]

In the research of MOEA (Multi-Objective Evolutionary Algorithm), many algorithms for multi-objective optimization have been proposed. Diversity of the solutions is an important measure, and it is also significant how to evaluate the diversity of an MOEA. In this paper, the clustering algorithm based on the distance between individuals is discussed, and a diversity metric based on clustering is also proposed. Applying this metric, we compare several popular multi-objective evolutionary algorithms. It is shown by experimental results that the method proposed in this paper performs well, especially helps to provide a comparative evaluation of two or more MOEAs. [ABSTRACT FROM AUTHOR]

This paper proposes an improved genetic algorithm for producing aesthetically pleasing drawings of general undirected graphs. Previous undirected graph drawing algorithms draw large cycles with no chords as concave polygons. In order to overcome such disadvantage, the genetic algorithm in this paper designs a new mutation operator single-vertex- neighborhood mutation and adds a component aiming at symmetric drawings to the fitness function, and it can draw such type graphs as convex polygons. The improved algorithm is of following advantages: The method is simple and it is easy to be implemented, and the drawings produced by the algorithm are beautiful, and also it is flexible in that the relative weights of the criteria can be altered. The experiment results show that the drawings of graphs produced by our algorithm are more beautiful than those produced by simple genetic algorithms, the original spring algorithm and the algorithm in bibliography [4]. [ABSTRACT FROM AUTHOR]

In the standard GA, the individual has no intelligence and must act upon some rules established by a programmer in advance, such as various genetic operator. The result is to make the evolutionary process to be trapped into the local optimization of the objective function. In order to solve this problem, through studying the structure of an agent and selection operator, the paper designs a new genetic algorithm based on agent, called AGA (Agent-based Genetic Algorithm). At the premise of giving the definition of the outer environment where an agent lives and of an agent's belief, this paper gives some rules on how an agent selects one agent to cross their genes and some rules on how to solve competition. In addition, a communication method based on blackboard is presented to solve the communication among the agent society. Finally, the paper gives the structure of AGA and the simulation result for a multi-peak function, which demonstrates the validity of the AGA. [ABSTRACT FROM AUTHOR]

Using an agent-based multi-asset artificial stock market, we simulate the survival dynamics of investors with different risk preferences. It is found that the survivability of investors is closely related to their risk preferences. Among the eight types of investors considered in this paper, only the CRRA investors with RRA coefficients close to one can survive in the long run. Other types of agents are eventually driven out of the market, including the famous CARA agents and agents who base their decision on the capital asset pricing model. [ABSTRACT FROM AUTHOR]

In this paper, we present a modified fuzzy min-max neural network model and its application to feature analysis. In the model a hyperbox can be expanded without considering the hyperbox contraction process as well as the overlapping test. During the learning process, the feature distribution information is utilized to compensate the hyperbox distortion which may be caused by eliminating the overlapping area of hyperboxes in the contraction process. The weight updating scheme and the hyperbox expansion algorithm for the learning process are described. A feature analysis technique for pattern classification using the model is also presented. We define four kinds of relevance factors between features and pattern classes to analyze the saliency of the features in the learning data set. [ABSTRACT FROM AUTHOR]

The objective of this paper is to use the back-propagation (BP) algorithm in conjunction with grey relations to find the optimal partitions of the consequent part in fuzzy neural networks (FNN). A BP algorithm with grey relational coefficient (GRC) is proposed in order to decrease the square errors of the FNN for acquiring the optimal partitions of the consequent part of fuzzy rules. From the simulation results, we find that the present method applied for fuzzy logic control of an inverted pendulum has better performance than that of the traditional BP algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a novel evolutionary algorithm based on adaptive multiple fitness functions and adaptive objective space division for multiobjective optimization is proposed. It can overcome the shortcoming of those using the weighted sum of objectives as the fitness functions, and find uniformly distributed solutions over the entire Pareto front for non-convex and complex multiobjective programming. First, we divide the objective space into multiple regions with about the same size by uniform design adaptively, then adaptively define multiple fitness functions to search these regions, respectively. As a result, the Pareto solutions found on each region are adaptively changed and eventually are uniformly distributed over the entire Pareto front. We execute the proposed algorithm to solve five standard test functions and compare performance with that of four widely used algorithms. The results show that the proposed algorithm can generate widely spread and uniformly distributed solutions over the entire Pareto front, and perform better than the compared algorithms. [ABSTRACT FROM AUTHOR]

In this paper, six different approaches using genetic algorithms (GA) and/or simulated annealing (SA) with improved bottom left (I-BL) algorithm [1] were applied for solution of two dimensional non-guillotine cutting problems. As examples, test problems including 29 individual rectangular pieces were used [2]. Performances of hybrid approaches on solutions of cutting problems were compared. Due to combined global search feature of GA and local search feature of SA, the hybrid approach using GA and SA yields the best results for these problems. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to further refine and validate clusters using a multi-label voting algorithm to identify and classify similar objects by neuro-fuzzy classifier ensembles. The algorithm uses predictions of neuro-fuzzy experts trained on provisional clusters of heterogeneous collections of data. The multi-label predictions of the modular ensemble of classifiers are further combined, using fuzzy aggregation techniques. The proposed refinement algorithm considers then the votes, triggered by the confirmation of the classifiers' expertise for voted labels, and updates the clustering solution. Experiments on a Visual Arts objects database of color features show better interpretations and performances of the clusters inferred by the proposed algorithm. Its results can be widely used in various classification and clustering applications. [ABSTRACT FROM AUTHOR]

To counteract software aging, a new nested software rejuvenation policy is put forward in this paper. Comparing to the conventional periodic software rejuvenation policy, the nested policy takes into account the application-level and system-level rejuvenation simultaneously and executes N times application-level rejuvenation before system-level rejuvenation. If any application-level rejuvenation is not executed successfully, then the system has to be rebooted directly. Comparing the minimum average rejuvenation cost per year and the maximum system availability of the nested software rejuvenation policy with the conventional periodic software rejuvenation policy's, the results demonstrate that the new policy consumes less downtime and lower rejuvenation cost, and enhances software availability and reliability. [ABSTRACT FROM AUTHOR]

A novel classification method based on regression is proposed in this paper and then the equivalences of the classification and regression are demonstrated by using numerical experiments under the framework of support vector machine. The proposed algorithm implements the classification tasks by the way used in regression problems. It is more efficiently for multi-classification problems since it can classify all samples at a time. Numerical experiments show that the two classical machine learning problems (classification and regression) can be solved by the method conventionally used for the opposite problem and the proposed regression-based classification algorithm can classify all samples belonging to different categories concurrently with an agreeable precision. [ABSTRACT FROM AUTHOR]

In completeness theories of multiple-valued logic, the characterization of Sheffer functions is an important problem, and the solution can be reduced to determining the minimal coverings of precomplete categories. It'fs well known that each precomplete set is a function set, T(Gm), preserving the relation Gm, therefore, the categorizing of this relation has provided the determination of precomplete set's minimal covering with more convenient ways. In this paper, simply separable relations in partial four-valued logic are categorized by similar relation. [ABSTRACT FROM AUTHOR]

Medicine analysis becomes more and more important in our production and life, especially the composition analysis for medicines. Available data are characterized by small amount and high dimensionality. Support vector machine (SVM) is an ideal algorithm for dealing with this kind of data. This paper presents a combined method of principal component analysis (PCA) and least square support vector machine (LS-SVM) to deal with the work of medicine composition analyses. The proposed method is applied to practical problems. Experiments demonstrate the predominance of the proposed method on both running time and prediction precision. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new machine learning approach based on AFS (Axiomatic Fuzzy Sets) fuzzy logic, in attempt to provide a better model with interpretability. First, we will concisely present the AFS theory. Second, we will propose new membership functions for fuzzy sets and their logic operations. Third, we will design a new machine learning algorithm based on the new membership functions and their logic operations. This algorithm has two advantages. One is that it can mimic the human reasoning comprehensively and offers a far more flexible and effective means for the study of large-scale intelligent systems. Another is its simplicity in implementation and mathematical beauty in fuzzy theory. Finally, a credit data example is used to illustrate its effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, a physiological fuzzy neural network is proposed, which shows more improved learning time and convergence property than that of the conventional fuzzy neural network. First, we investigate the structure of physiological neurons of the nervous system and propose new neuron structure based on fuzzy logic. And by using the proposed fuzzy neuron structures, the model and learning algorithm of physiological fuzzy neural network are proposed. We applied the proposed algorithm to 3-bit parity problem. The experiment results showed that the proposed algorithm reduces the possibility of local minima more than the conventional single layer perceptron does, and improves the time and convergence for learning. [ABSTRACT FROM AUTHOR]

This paper presents the implementation of adaptive fuzzy neural network controller (FNNC) for accurate speed control of a permanent magnet synchronous motor (PMSM). FNNC includes neural network controller (NC) and fuzzy logic controller (FC). It combines the capability of fuzzy reasoning in handling uncertain information and the capability of neural network in learning from processes. The initial weights and biases of the artificial neural network (ANN) are obtained by offline training method. Using the output of the fuzzy controller (FC), online training is carried out to update the weights and biases of the ANN. Several results of simulation are provided to demonstrate the effectiveness of the proposed FNNC under the occurrence of parameter variations and external disturbance. [ABSTRACT FROM AUTHOR]

In [1], a variant version of the fuzzy cellular neural network, called FCNN, is proposed to effectively segment microscopic white blood cell images. However, when applied to the segmentation of serial CT liver images, it does not work well. In this paper, FCNN is improved to be the novel neural network —Advanced Fuzzy Cellular Neural Network AFCNN. Just like FCNN, AFCNN still keeps its convergent property and global stability. When applied to segment serial CT liver images, AFCNN has the distinctive advantage over FCNN: it can keep boundary integrity better and have better recall accuracies such that the segmented images can approximate original liver images better. [ABSTRACT FROM AUTHOR]

Min-implication fuzzy relation equations based on Boolean-type implications can also be viewed as a way of implementing fuzzy associative memories with perfect recall. In this paper, fuzzy associative memories with perfect recall are constructed, and new on-line learning algorithms adapting the weights of its interconnections are incorporated into this neural network when the solution set of the fuzzy relation equation is non-empty. These weight matrices are actually the least solution matrix and all maximal solution matrices of the fuzzy relation equation, respectively. The complete solution set of min-implication fuzzy relation equation can be determined by the maximal solution set of this equation. [ABSTRACT FROM AUTHOR]

This paper addresses an obstacle avoidance issue for redundant nonholonomic mobile modular manipulators. On the basis of modular robot concept, an integrated dynamic modeling method is proposed, which takes both the mobile platform and the onboard modular manipulator as an integrated structure. A new obstacle avoidance algorithm is proposed which is mainly composed of two parts: a self-motion planner (SMP) and a robust adaptive neural fuzzy controller (RANFC). One important feature of this algorithm lies in that obstacles are avoided by online adjusting self-motions so that the end-effector task will not be affected unless the obstacles are just on the desired trajectory. The RANFC does not rely on exact aprior dynamic parameters and can suppress bounded external disturbance effectively. The effectiveness of the proposed algorithm is verified by simulations. [ABSTRACT FROM AUTHOR]

In this paper, real time motion tracking of a robot manipulator based on the adaptive learning radial basis function network is proposed. This method for adaptive learning needs little knowledge of the plant in the design processes. So the centers and widths of the employed radial basis function network (RBFN) as well as the weights are determined adaptively. With the help of the RBFN, motion tracking of the robot manipulator is implemented without knowing the information of the system in advance. Furthermore, identification error and the tuned parameters of the RBFN are guaranteed to be uniformly ultimately bounded in the sense of Lyapunov's stability criterion. [ABSTRACT FROM AUTHOR]

In this paper, a semi-actively tuned liquid column damper (TLCD) based on fuzzy neural networks (FNN) is proposed to vibration control of irregular buildings excited by multi-dimensional earthquake ground motions. The fuzzy neural networks method takes advantage of both neural networks and fuzzy controls and has the unique combination of ability to learn via nonlinear mapping of neural nets and the capacity to integrate expert knowledge via fuzzy rules. The fuzzy neural networks based on Takagi-Sugeno model is adopted in this research to actively adjust the orifice opening-area of the TLCD. An eccentric building equipped with two TLCDs arranged in perpendicular directions is used as an object for suppressing vibrations induced by multi-dimensional earthquake ground motions. For numerical simulations, a state space representation of the building-TLCD system is derived. Numerical simulations demonstrate that TLCDs regulated by the fuzzy neural networks are effective in controlling both the translational and rotational seismic response of the eccentric building. [ABSTRACT FROM AUTHOR]

In this paper we focus on run-time optimization of the IEEE 802.11 protocol to improve its performance using a well-known fuzzy logic approach. Specifically, we derive the simple, and more accurate, approximation of the network contention level and the average size of contention window to maximize the theoretical throughput limit. In addition, we propose and evaluate a new distributed fuzzy contention control mechanism that is a modification of the asymptotically optimal backoff (AOB) mechanism using a fuzzy logic approach. The proposed mechanism can be used to extend the standard 802.11 access mechanism without requiring any additional hardware like the AOB mechanism. To verify efficiency of our mechanism, the performance of the IEEE 802.11 standard protocol with the AOB and the proposed mechanism are investigated through simulations. [ABSTRACT FROM AUTHOR]

Resistance spot welding (RSW) is widely utilized as a joining technique for automobile industry. However, good weld quality control method has not yet been developed in plant environment when part fitup fault exists. This paper proposed a neuro-fuzzy algorithm to control weld quality by adjusting welding current. An experimental system was developed to measure electrode displacement curve. Accordingly based on electrode displacement curve optimal current for every cycle will be achieved under poor fitup fault condition. Results showed that proposed neuro-fuzzy system is suitable as a weld quality monitoring for resistance spot welding. [ABSTRACT FROM AUTHOR]

Parallel genetic algorithms are usually implemented on parallel machines or distributed systems. This paper describes how fine-grained parallel genetic algorithms can be mapped to programmable graphics hardware found in commodity PC. Our approach stores chromosomes and their fitness values in texture memory on graphics card. Both fitness evaluation and genetic operations are implemented entirely with fragment programs executed on graphics processing unit in parallel. We demonstrate the effectiveness of our approach by comparing it with compatible software implementation. The presented approach allows us benefit from the advantages of parallel genetic algorithms on low-cost platform. [ABSTRACT FROM AUTHOR]

In this paper the neuron operation using stimulated Brillouin scattering (SBS) in optical fiber is described. The inherent optical feedback by the backscattered Stokes wave in optical fiber leads to instabilities in the form of optical chaos. At low power, the nature of the Brillouin instability can occur below threshold. At high power, the temporal evolution above SBS threshold is periodic and can become chaotic. Control of chaos induced transient instability in Brillouin-active fiber is experimentally implemented with Kerr nonlinearity having a non-instantaneous response in netowork systems. Controlling chaotic instabilities can lead to multistable periodic states; create optical logic 'on' or high level "1" or 'off, or low level "0". It can be used in neural networks. It can also lead, in principle, to large memory capacity. [ABSTRACT FROM AUTHOR]

High Quality CD, and DAT audio is very data intensive. Currently, the multi-channel technique is the preferred method of audio transmission. The MPEG(Moving Picture Experts Group) provides data compression technology for sound and image systems. The MPEG-2 AAC standard provides multi-channel 5.1 sound, using the same audio algorithm as MPEG-1, thus MPEG-2 audio both forward and backward compatible. The MDCT(Modified Discrete Cosine Transform)is a linear orthogonal lapped transform based on the concept of TDAC(Time Domain Aliasing Cancellation). In this paper, we propose an efficient algorithm for the optimization of the core in the audio part of the data transmission based on the MDCT/IMDCT(Inverse MDCT). This algorithm reduced the operating coefficient by overlapped area to bind. In the comparison of the original algorithm with the optimized algorithm that cosine coefficient reduced 0.5%, multiplies operating 0.098% and adds operating 0.58%. The proposed Algorithm was implemented using the C language then we designed hardware architecture of micro-programmed method it's applied to optimized algorithm. This processor was designed with the VHDL language and was synthesized using the design analyzer of SYNOPSYS, with rule checking by SADAS. This processor operates at a clock frequency of 20MHz and a voltage of 5V. Thus, the designed system can be used for systems based on other FPGA and ASIC. [ABSTRACT FROM AUTHOR]

This paper proposes several improved CMOS analog circuits for neuro-fuzzy network, including Gaussian-like membership function circuit, minimization circuit, and a centroid algorithm defuzzier circuit without using division. A two-input/one-output neuro-fuzzy network composed of these circuits is implemented and testified for non-linear function approximating. HSPICE simulation results show that all the proposed circuits provide characteristics of high operation capacity, high speed, simple structures, and high precision. They are very suitable for rapid implementation of neuro-fuzzy networks. [ABSTRACT FROM AUTHOR]

The need for high precision calculations in various scientific disciplines has led to development of systems with various solutions specific to the problem on hand. The complexity of such systems not withstanding, a generic solution could be the use of neural networks. To be able to leverage the best out of the neural network, hardware implementations are ideal as they give speed-up of several orders of magnitude over software simulations. A simple architecture for such a neuro-chip is proposed in this paper. The neuro-chip supports the current draft version of the IEEE-754 standard for floating-point arithmetic. The synthesis results indicate an estimated 84 MCUPS speed of operation. [ABSTRACT FROM AUTHOR]

Hierarchical convolutional neural networks are a well-known robust image-recognition model. In order to apply this model to robot vision or various intelligent real-time vision systems, its VLSI implementation is essential. This paper proposes a new algorithm for reducing multiply-and-accumulation operation by sorting neuron outputs by magnitude. We also propose a VLSI architecture based on this algorithm. We have designed and fabricated a sorting LSI by using a 0.35 μm CMOS process. We have verified successful sorting operations at 100 MHz clock cycle by circuit simulation. [ABSTRACT FROM AUTHOR]

One problem facing many existing OLAP (On-Line Analytical Process) systems is the so-called ragged dimensions. Ragged dimensions occur if the logical parents of some members in a dimension hierarchy are two or more levels apart. In other words, there exist empty holes in the dimension hierarchy. The problems caused by ragged dimension are two-fold. First, aggregation of measure data could be incorrect. Second, the pre-computation strategy, the most prevalent technique used to speed up query processing in current OLAP system, could be rendered invalid. This paper proposes a simple yet efficient solution to remedy the ragged dimension problem for existing OLAP systems. [ABSTRACT FROM AUTHOR]

Current techniques used to detect hacker intrusion are postmortem in that they get into action only if someone or something is intruding, in other words, they are reactionary. This paper proposes a PDE-based intrusion forecast model that aims to forecast hacker intrusion before they actually occur. [ABSTRACT FROM AUTHOR]

Human social and economic life is becoming increasingly dependent on computers and information technologies. Many important systems such as banking, tax filing, traffic control, and even military functions are now controlled by or receive data feed from computers. Hence, the protection of IT systems from natural and man-made disasters has taken on critical importance. This paper presents a framework for building self-surviving IT systems that can defend themselves against and survive natural and man-made disasters such as earthquake, flood, fire, virus, intrusion, or outright war. The work presented here is a partial result of an ongoing research project called HERMES IT Shield we are conducting at Shanghai Jiao Tong University, China. [ABSTRACT FROM AUTHOR]

Multisensory fusion is being increasing viewed as an important activity in the filed of mobile robot navigation and obstacle avoidance. The fusion of data from a variety of sensors makes the mobile robot more easily survival in a hostile environment. It takes advantage of the redundancy and reciprocity of multisensory data and increases the precision and reliability of inference and judgment for the mobile robot. This paper presents a method which employs fuzzy logic and neural networks to fuse data from several kinds of sensors. As a result, more exact navigation and quick obstacle avoidance can be achieved. [ABSTRACT FROM AUTHOR]

In this paper, we propose the steganalysis based on sequence synchronization analysis against chaos based spread spectrum image steganography (CSSIS). This method uses the correlation between the estimated chaotic sequences in two stegoimages to buildup synchronization measure, which can effectively detect the presence of CSSIS. Based on the analysis, a more secure method is presented, which is constructed on key transmission channel (KTC). This improved method uses the stochastic modulation to realize the steganography. It avoids the sequences synchronization fault in CSSIS by randomly choosing the parameters of chaotic map, which is proved by the experimental results. [ABSTRACT FROM AUTHOR]

A new blind watermarking algorithm is proposed in this paper. Our watermark embedding algorithm mainly exploits the important properties of Singular Value Decomposition (SVD). By means of Independent Component Analysis (ICA), the watermark is successfully extracted without the original image. Experiment results have shown that the proposed approach is robust against the common signal processing and geometric attacks. [ABSTRACT FROM AUTHOR]

In this paper, we propose a steganographic technique on images that provides high capacity of secret information as well as imperceptibility of stego image. Our method inserts secret data into every pixel of the image and decides the number of insertion bits using the difference value between two pixels adjacent to the target pixel. Therefore, the number of insertion bits in each pixel is dependent on whether the target pixel is included in an edge area or a smooth area. The experiment results show that the proposed method provides more efficient performance than that of the existing methods from the viewpoint of both the insertion amount and the visual measures. [ABSTRACT FROM AUTHOR]

A standard cell placement algorithm based on adaptive simulated annealing is presented in this paper. Considering the characters of different circuits to be placed, adaptively initial temperature and adaptive searching region are added to traditional simulated annealing algorithm. At the same time, the punishment item in objective function and initial placement approach are improved for the standard cell placement problem. This algorithm is applied to test a set of benchmark circuits, and experiments reveal its advantages in placement results and time performance when compared with the traditional simulated annealing algorithm. [ABSTRACT FROM AUTHOR]

Panoramic Video which uses 360 degree panoramic image is a new approach for composing virtual environment. The panoramic images can be created by "stitching" together overlapping images taken with an ordinary camera. So image mosaics are very important in creating panorama. In this paper, we proposed an intelligent mosaics algorithm. We first use particle swarm optimization (PSO) to find a certain area which contains sufficient objective characters, then we use pattern matching method to search the matching patch in another image and adjust image; at last, the mosaic image is created by a multi-resolution method. Experimental results testy that this algorithm is able to seamlessly stitch two overlapping images automatically. [ABSTRACT FROM AUTHOR]

In this paper, a novel optimal design method for PID controller is proposed based on the ant system (AS) algorithm. In this method, for a given control system with a PID controller, by taking the overshoot, settling time, and steady-state error of unit step response of the system as the performance indexes and using the AS algorithm, the optimal PID controller parameters Kp*, Ti*, and Td* can be obtained. The proposed method has excellent features, including easy implementation, good convergence property, and efficient tuning of PID controller parameters. The PID controller designed using this method is called the AS-PID controller. In order to verify the good performance of the AS-PID controller, four typical control systems were tested. The simulation results show that the proposed method is indeed adaptive and robust in quick search of the optimal PID controller parameters. [ABSTRACT FROM AUTHOR]