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This paper is concerned with a multi-resolution tool for analysis of a time series aiming to detect abnormalities in various frequency regions. The task is treated as a kernel based novelty detection applied to a multi-level time series representation obtained from the discrete wavelet transform. Having a priori knowledge that the abnormalities manifest themselves in several frequency regions, a committee of detectors utilizing data dependent aggregation weights is build by combining outputs of detectors operating in those regions. [ABSTRACT FROM AUTHOR]

One of the simplest protocols for clock synchronization in wireless ad hoc and sensor networks is the converge-to-max protocol, which has the simple logic of adjusting each node's clock to be at least as large as any neighbor's. This paper examines the converge-to-max protocol, showing it to be stabilizing even when node clocks have skew, bounded domains, and dynamic communication links. [ABSTRACT FROM AUTHOR]

Reputation systems play an important role in many Internet communities. They allow individuals to estimate other individual's behavior during interactions. However, a more privacy-friendly reputation system is desirable while maintaining its trustworthiness. This paper presents a fair anonymous submission and review system. The review process is reputation-based and provides better anonymity properties than existing reputation systems. Moreover, the system allows for accountability measures. Anonymous credentials are used as basic blocks. [ABSTRACT FROM AUTHOR]

Most, if not all, conferences use an online system to handle paper submissions and reviews. Introduction of these systems has significantly facilitated the administration, submission and review process compared to traditional paper-based ones. However, it is crucial that these systems have strong resistance against Web attacks as they involve confidential data and privacy. Some submissions could be leading edge breakthroughs that authors do not wish to leak out and be subtly plagiarized. Also, security of the employed system will attract more submissions to conferences that use it and gives confidence of the quality that the conferences uphold. In this paper, we analyze the security of the Web-Submission-and-Review (WSaR) software - latest version 0.53 beta at the time of writing; developed by Shai Halevi from IBM Research. WSaR is currently in use by top cryptology and security-related conferences including Eurocrypt 2007 & 2008, Crypto 2007, and Asiacrypt 2007, annually sponsored by the International Association for Cryptologic Research (IACR). We present detailed analysis on WSaR's security features. In particular, we first discuss the desirable security features that are designed into WSaR and what attacks these features defend against. Then, we discuss how some untreated security issues may lead to problems, and we show how to enhance WSaR security features to take these issues into consideration. Our results are the first known careful analysis of WSaR, or any type of online submission system for that matter. [ABSTRACT FROM AUTHOR]

Several features of today's grid are based on centralized or hierarchical services. However, as the grid size increasing, some of their functions especially resource discovery should be decentralized to avoid performance bottlenecks and guarantee scalability. A novel grid resource discovery method based on adaptive k-Nearest Neighbors clustering is presented in this paper. A class is formed by a collection of nodes with some similarities in their characteristics, each class is managed by a leader and consists of members that serve as workers. Resource requests are ideally forwarded to an appropriate class leader that would then direct it to one of its workers. This method can handle resource requests by searching a small subset out of a large number of nodes by resource clustering which can improve the resource query efficiency; on the other hand, it also achieves well scalability by managing grid resources with adaptive mechanism. It is shown from a series of experiments that the method presented in this paper achieves more scalability and efficient lookup performance than other existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of scheduling a set of n periodic preemptive independent hard real-time tasks on the minimum number of processors. We assume that the partitioning strategy is used to allocate the tasks to the processors and the EDF method is used to schedule the tasks on each processor. It is known that this scenario is NP-hard; thus, it is unlikely to find a polynomial time algorithm to schedule the tasks on the minimum number of processors. In this work, we derive a lower and an upper bound for the number of processors required to satisfy the constraints of our problem. We also compare a number of heuristic algorithms with each other and with the bounds derived in this paper. Numerical results demonstrate that our lower bound is very tight and it is very close to the optimal solution. [ABSTRACT FROM AUTHOR]

In this paper we propose a two-pass classification method and apply it to face recognitions. The method is obtained by integrating together two approaches, the hyper-ellipsoid neural networks (HENN's) and the SVM's with error correcting codes. This method realizes a classification operation in two passes: the first one is to get an intermediate classification result for an input sample by using the HENN's, and the second pass is followed by using the SVM's to re-classify the sample based on both the input data and the intermediate result. Simulations conducted in the paper for applications to face recognition showed that the two-pass method can maintain the advantages of both the HENN's and the SVM's while remedying their disadvantages. Compared with the HENN's and the SVM's, a significant improvement of recognition performance over them has been achieved by the new method. [ABSTRACT FROM AUTHOR]

The Software Engineering track received 63 papers from which 7 papers were selected after an intensive reviewing and selection process. Many good papers could not be selected due to lack of space in the program. The selected papers cover a diverse range of topics within software engineering: from software reliability prediction to middle-ware for component management to runtime validation and code generation. The paper by Roychoudhury, Negi and Mitra analyzes programs loops for estimating program execution time. They use constraint propagation techniques to detect infeasible paths followed by timing analysis that employ memoization techniques. The paper by Sengupta and Cleaveland presents the operational semantics of timed message sequence charts to help detect errors and inconsistencies in specifications. Tripathi and Mall present a method for making predictions about reliability of software during the software development process itself when the failure data from the field cannot be available. The paper by Wang presents a logic programming framework for integrating architecture description languages (ADLs) which allows tools developed for one ADL to be used even though the architectural specification is written in another ADL. In a similar vain, the paper by Stevenson, Fu and Dong presents a framework for automated and validated realization of software architecture designs. The paper by Bhattarcharjee and Shyamsundar presents a method for validated code generation for activity diagrams which are useful in model driven design of software. Finally, the paper by Mousavi et al presents techniques that exploit symmetry for tackling the state-space explosion problem that arises in model checking. [ABSTRACT FROM AUTHOR]

This paper introduces a new paradigm to realize various types of cryptographic primitives such as authenticated key exchange and key encapsulation in the standard model under three standard assumptions: the decisional Diffie-Hellman (DDH) assumption, target collision resistant (TCR) hash functions and pseudo-random functions (PRFs). We propose the first (PKI-based) two-pass authenticated key exchange (AKE) protocol that is comparably as efficient as the existing most efficient protocols like MQV and that is secure in the standard model (under these standard assumptions), while the existing efficient two-pass AKE protocols such as HMQV, NAXOS and CMQV are secure in the random oracle model. Our protocol is shown to be secure in the (currently) strongest security definition, the extended Canetti-Krawczyk (eCK) security definition introduced by LaMacchia, Lauter and Mityagin. This paper also proposes a CCA-secure key encapsulation mechanism (KEM) under these assumptions, which is almost as efficient as the Kurosawa-Desmedt KEM. This scheme is also secure in a stronger security notion, the chosen public-key and ciphertext attack (CPCA) security. The proposed schemes in this paper are redundancy-free (or validity-check-free) and the implication is that combining them with redundancy-free symmetric encryption (DEM) will yield redundancy-free (e.g., MAC-free) CCA-secure hybrid encryption. [ABSTRACT FROM AUTHOR]

Let consider n autonomous mobile robots that can move in a two dimensional plane. The gathering problem is one of the most fundamental tasks of autonomous mobile robots. In short, given a set of robots with arbitrary initial locations, gathering must make all robots meet in finite time at a point that is not predefined. In this paper, we study about the feasibility of gathering by mobile robots that have φ-absolute error dynamic compasses. While the direction of each local coordinate system is fixed in usual systems, the dynamic compass model allows the angle difference between a local coordinate system and the global coordinate system to vary with time in the range of [0, φ]. This paper proposes a semi-synchronous gathering algorithm for n robots with (π/2 − ε)-absolute error dynamic compasses, where ε is an arbitrary small constant larger than zero. To the best of our knowledge, the proposed algorithm is the first one that considers both inaccurate compass models and more than two robots. We also show the optimality of our algorithm. It is proved that for any φ ≥ π/2, there is no algorithm to gather two robots with φ-absolute error dynamic compasses. [ABSTRACT FROM AUTHOR]

This paper presents an approach for solving the driving load forecasting problem based on Cascade Neural Networks with node-decoupled extended Kalman Filtering (CNN-NDEKF). Because of the inherent advantages, hybrid electric vehicles (HEV) are being given more and more attention. The power control strategy of HEVs is the key technology which determines the HEV's efficiency and pollutive emission level. Since the extent of improvement involved with HEV power control strategies greatly depends on the future driving load forecasting, in this paper, we attempt to achieve driving load forecasting using CNN-NDEKF. Instead of forecasting the entire load sequence, we define 5 load levels by a fuzzy logic method and then we forecast the load level. Simulation study is given to illustrate the feasibility of the driving load forecasting approach. [ABSTRACT FROM AUTHOR]

Aiming at improving the convergence performance of conventional BP neural network, this paper presents an improved PSO algorithm instead of gradient descent method to optimize the weights and thresholds of BP network. The strategy of the algorithm is that in each iteration loop, on every dimension d of particle swarm containing n particles, choose the particle whose velocity decreases most quickly to mutate its velocity according to some probability. Simulation results show that the new algorithm is very effective. It is successful to apply the algorithm to gas turbine fault diagnosis. [ABSTRACT FROM AUTHOR]

In this paper, we study the Cohen-Grossberg neural networks with mixed time-varying delays. By applying the Lyapunov functional method and combining with the inequality 3abc ≤ a3 + b3 + c3 (a,b,c > 0) technique, a series of new and useful criteria on the existence of equilibrium point and its global asymptotical stability are established. The results obtained in this paper extend and generalize the corresponding results existing in previous literature. [ABSTRACT FROM AUTHOR]

This paper studies a general class of neural networks with time-varying delays and the neuron activations belong to the set of discontinuous monotone increasing functions. The discontinuities in the activations are an ideal model of the situation where the gain of the neuron amplifiers is very high. Because the delay in combination with high-gain nonlinearities is a particularly harmful source of potential instability, in the paper, conditions which ensure the global convergence of the neural network are derived. [ABSTRACT FROM AUTHOR]

Electromagnetic (EM)-based neural network (NN) approaches have recently gained recognition as unconventional and useful methods for radio frequency (RF) components modeling. In this paper, several improvement techniques including a new data preprocessing technique and an improved training algorithm are presented. Comprehensive cases are compared in this paper. The experimental results indicate that with these techniques, the modified model has better performance. [ABSTRACT FROM AUTHOR]

The digital watermark is used for detection of digital image alteration. However, most of digital images are printed on the paper document for submitting. Once digital images are printed on the paper, it is hard to detect alteration of it. In this paper, the detection method of printed image alteration by using digital watermark is proposed. [ABSTRACT FROM AUTHOR]

An efficient searching system is needed to offer the exact result of diverse web information to the user. Due to this reason, it is important to extract and analyze the user requirements in the distributed information environment. The searching method proposed in this paper uses the keyword as well as its context information for effective searching. Moreover, the proposed searching method is extracted keywords by using the new keyword extraction method also proposed in this paper, and it is executed web searching based on keyword mining profile generated by the extracted keywords. Unlike the conventional searching method, which searched for information by representative words, the proposed searching method is more efficient and exact. This is because data are searched by the example-based query including the content information as well as the representative words. Moreover, this searching method makes a domain keyword list for a quick search. The domain keyword is the representative word of a special domain. The performance of the proposed algorithm is analyzed in a series of experiments to identify its various characteristics. [ABSTRACT FROM AUTHOR]

Raster map should be corrected after scanned because of the errors caused by paper map deformation. In the paper, the deficiency of the polynomial fitting method is analyzed. The paper introduces an ANN (Artificial Neural Network) correcting method that utilizes the advantage of its function approximation ability. In the paper, two types of ANNs, BP and GRNN, are designed for the correcting. The comparing experiment is done with the same data by the polynomial fitting and ANN methods, utilizing the MALAB. The experiment results show that the ANN methods, especially the GRNN method, performances far better than the polynomial fitting method does. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to design a Conformance Test Suite(CTS) for BSPs(Biometric Service Provider) based upon the BioAPI (Biometric Application Programming Interface) v2.0, an international standard by ISO/IEC JTC1/SC37. The proposed BioAPI CTS enables users to test BSPs without depending on various frameworks. In this paper, a test scheduling tool has been embodied in order to use Test Assertion with XML. In order to demonstrate the performance of the CTS, the experiment was performed using both commercial fingerprint verification and identification BSPs. The developed CTS will be installed at Korean National Biometrics Test Center and used to test whether commercial biometrics products are compliant to BioAPI. [ABSTRACT FROM AUTHOR]

The k-set agreement problem is a generalization of the uniform consensus problem: each process proposes a value, and each non-faulty process has to decide a value such that a decided value is a proposed value, and at most k different values are decided. It has been shown that any algorithm that solves the k-set agreement problem in synchronous systems that can suffer up to t crash failures requires $\lfloor \frac{t}{k} \rfloor +1$ rounds in the worst case. It has also been shown that it is possible to design early deciding algorithms where no process decides and halts after $\min\big( \lfloor \frac{f}{k} \rfloor +2, \lfloor \frac{t}{k} \rfloor +1\big)$ rounds, where f is the number of actual crashes in a run (0 ≤ f ≤ t). This paper explores a new direction to solve the k-set agreement problem in a synchronous system. It considers that the system is enriched with base objects (denoted [m,ℓ]_SA objects) that allow solving the ℓ-set agreement problem in a set of m processes (m < n). The paper has several contributions. It first proposes a synchronous k-set agreement algorithm that benefits from such underlying base objects. This algorithm requires $O(\frac{t \ell}{m k})$ rounds, more precisely, $R_t= \lfloor \frac{t}{\Delta} \rfloor +1$ rounds, where $\Delta= m \lfloor \frac{k}{\ell} \rfloor + (k\mbox{ mod } \ell)$. The paper then shows that this bound, that involves all the parameters that characterize both the problem (k) and its environment (t, m and ℓ), is a lower bound. The proof of this lower bound sheds additional light on the deep connection between synchronous efficiency and asynchronous computability. Finally, the paper extends its investigation to the early deciding case. It presents a k-set agreement algorithm that directs the processes to decide and stop by round $R_f=\min\big(\lfloor \frac{f}{\Delta} \rfloor +2, \lfloor \frac{t}{\Delta} \rfloor +1\big)$. These bounds generalize the bounds previously established for solving the k-set problem in pure synchronous systems. [ABSTRACT FROM AUTHOR]

In composing and using services, user's requirements are subject to uncertainty and changes. It can be difficult for users to maintain an overview of all available services and to make good choices among them. This paper proposes an approach to proactively recommending suitable services to users. Our major contribution is to have devised a novel user-interest model to describe user's interests adaptively. A reasonable way is put forward for picking up suitable services timely and its key problem is defined formally. Important properties of the model are theoretically proved, and the effectiveness of recommendations is verified with prototypical implementation and tryouts in public service area. [ABSTRACT FROM AUTHOR]

This paper considers the problem of unicasting in wireless ad hoc networks. Unicasting is the problem of finding a route between a source and a destination and forwarding the message from the source to the destination. In theory, models that have been used oversimplify the problem of route discovery in ad hoc networks. The achievement of this paper is threefold. First we use a more general model in which nodes can have different transmission and interference ranges and we present a new routing algorithm for wireless ad hoc networks that has several nice features. We then combine our algorithm with that of known greedy algorithms to arrive at an average case efficient routing algorithm in the situation that GPS information is available. Finally we show how to schedule unicast traffic between a set of source-destination pairs by providing a proper vertex coloring of the nodes in the wireless ad hoc network. Our coloring algorithm achieves a O(Δ)-coloring that is locally distinct within the 2-hop neighborhood of any node. [ABSTRACT FROM AUTHOR]

In this paper we consider a problem of privacy preserving surveillance and monitoring in sensor networks. We propose a framework and a solution of the problem based on multi-party computations. Our approach use pattern matching on data streams from sensors in order to monitor and detect events of interest. We study a privacy preserving pattern matching problem where patterns are defined as sequences of constraints on input data items. We describe a new privacy preserving pattern matching algorithm over an infinite alphabet A where a pattern P is given as a sequence $\{p_{i_1},p_{i_2},...,p_{i_n}\}$ of predicates $p_{i_j}$ defined on A. The algorithm addresses the following problem: given a pattern P and an input sequence t, find privately all positions in t where P matches t. The privacy preserving in the context of this paper means that sensor measurements will be evaluated as predicates pi(ej) privately, that is, sensors will not need to disclose the measurement values to the monitor. [ABSTRACT FROM AUTHOR]

There has been significant effort to build high throughput computing systems out of many distributed multimedia servers. These systems should accommodate a larger number of servers and should handle the ever-growing user demands. Recently, Grid computing has become one of the promising technologies that can deliver such a system. Among the many problems encountered in building Grid computing systems, resource discovery, overhead reduction, workload balancing and fault tolerance are the most challenging. In this paper, we present a resource discovery and allocation mechanism (RDAM) in a computational Grid system. [ABSTRACT FROM AUTHOR]

In this paper, we present some new results on impossible differential cryptanalysis of reduced AES, which update the best known impossible differential attacks on reduced AES. First, we present some new attacks on 6-round AES (for all the three key length). Second, we extend to 7-round AES, also for all the three key variants. Especially for 128-bit keys, the best known results can attack up to 7 rounds using square attack and collision attack respectively, but their complexity are both marginal either on data or on time (ie. require nearly the entire codebook, or close to key exhaustive search). In this sense, our attack is the first non-marginal one on 7-round AES with 128-bit keys. Thirdly, we extend to 8 rounds for 256-bit keys, which is also non-marginal compared with the best non-related-key attacks so far. Finally, we give an improvement of the 7-round attack for 192-bit keys in R.C.W.Phan's paper, which makes the time complexity reduced greatly. [ABSTRACT FROM AUTHOR]

An optimization algorithm for dynamic Bayesian networks (DBN) based on Bayesian optimization algorithm (BOA) is developed for learning and constructing the DBN structure. In this paper, we first introduce some basic theories and concepts of probability model evolutionary algorithm. Then we describe, the basic mode for constructing DBN diagram and the mechanism of DBN structure learning based on BOA. The DBN structure learning based on BOA consists of two parts. The first part is to obtain the structure and parameters of DBN in terms of a good solution, and the second part is to produce new groups according to the obtained DBN structure. In this paper, the DBN learning is achieved by genetics algorithm based on a greedy mechanism. The DBN inference is performed by a forward-simulation algorithm. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Generalization problem is a key problem in NN society, which can be grouped into two classes: the generalization problem with unlimited size of training sample and that with limited size of training sample. The generalization problem with limited size of training sample is considered in this paper. Similar to margin maximization criterion in SVM, we propose a margin maximization training algorithm for BP network to further improve the generalization ability of BP network. Experimental results show that the margin maximization training algorithm proposed in this paper does improve the performance of BP network, and shows a comparable performance with SVM. [ABSTRACT FROM AUTHOR]

Chaotic noise cancellation has potential application in both secret communication and radar target identification. To solve the problem of parameter estimation in chaotic noise, a novel radial basis function neural network (RBF-NN) -based chaotic time series data modeling method is presented in this paper. Together with the spectral analysis technique, the algorithm combines neural network's ability to approximate any nonlinear function. Based on the flexibility of RBF-NN predictor and classical amplitude spectral analysis technique, this paper proposes a new algorithm for parameter estimation in chaotic noise. Analysis of the proposed algorithm's principle and simulation experiments results are given out, which show the effective of the proposed method. We conclude that the study has potential application in various fields as in secret communication for narrow band interference rejection or attenuation and in radar signal processing for weak target detection and identification in sea clutter. [ABSTRACT FROM AUTHOR]

We demonstrate in the paper that user movement has great influences on the system performance of mobile cellular networks. We also develop a system model to study to what extent the influences will be, with focusing on the user movement simulation. We concern the parameters that determine the system performance of mobile cellular networks. We show partially in this paper that these parameters vary while user movement changes, but the effects are interactive. Since handoff calls are given a higher priority over new calls and GC mechanism is employed, our analysis shows that a mature mobile cellular network should make use of user movement characteristics and dynamically adjust its reservation threshold of the handoff call requests. Extensive simulation results validate our analysis. [ABSTRACT FROM AUTHOR]

Recently, information communication market is reorganized focused on the digital convergence service used for integrating the communication, broadcasting and internet media by connecting the communication equipment, broadcasting equipment and computer via a network. The mobility support protocol enabling the effective handover between heterogeneous networks is emerged as the hot issue in relation with these next generation communication networks. In this paper, we are to suggest the MIH Vertical handover procedure required for the implementation of MIH over MIPv4 Low-Latency vertical handover by analyzing the handover performance in the overlap area between wireless networks using the vertical handover function of MIPv4 Mobile IP Technology of IETF and considering the expected problems. Also, we are to suggest the MIH L2-Trigger generation algorithm required for deciding the optimal handoff. We evaluate the vertical handover performance of MIPv4 low-latency in the overlap area between wireless networks by using the NS-2 and applying the IEEE 802.21 MIH (Media Independent Handover) based on the suggested technique and algorithm. [ABSTRACT FROM AUTHOR]

Architecting service-oriented systems is a complex design activity. It involves making trade-offs among a number of interdependent design decisions, which are drawn from a range of concerns by various software stakeholders. In order to achieve effective and efficient SOC design we believe a careful study of architectural styles that can form the reference architecture is important. Hence, this paper provides a study of architectural styles for the reference architecture of SOC-based software systems. We propose a classification scheme for the architecture styles. These architectural styles are extracted from existing research projects and industry practices based on our classification scheme. For all those identified styles, we present an evolution trend driven by engineering principles for Internet-scale systems. As a result, this paper moves the first step towards creating a Reference Architecture that can be utilised to provide sensible guidance on the design of Web services application architecture. [ABSTRACT FROM AUTHOR]

One of the outcomes of DARPA's HPCS program has been the creation of three new high productivity languages: Chapel, Fortress, and X10. While these languages have introduced improvements in language expressiveness and programmer productivity, several technical challenges still remain in delivering high performance with these languages. In the absence of optimization, the high-level language constructs that improve productivity can result in order-of-magnitude runtime performance degradations. This paper addresses the problem of efficient code generation for high level array accesses in the X10 language. Two aspects of high level array accesses in X10 are important for productivity but also pose significant performance challenges: the high level accesses are performed through Point objects rather than integer indices, and variables containing references to arrays are rank-independent. Our solution to the first challenge is to extend the X10 compiler with automatic inlining and scalar replacement of Point objects. Our partial solution to the second challenge is to use X10's dependent type system to enable the programmer to annotate array variable declarations with additional information for the rank and region of the variable, and to allow the compiler to generate efficient code in cases where the dependent type information is available. Although this paper focuses on high level array accesses in X10, our approach is applicable to similar constructs in other languages. Our experimental results for single-thread performance demonstrate that these compiler optimizations can enable high-level X10 array accesses with implicit ranks and Points to improve performance by up to a factor of 5.4× over unoptimized X10 code, and to also achieve performance comparable (from 48% to 100%) to that of lower-level Java programs. These results underscore the importance of the optimization techniques presented in this paper for achieving high performance with high productivity. [ABSTRACT FROM AUTHOR]

This paper presents an efficient scheme maintaining a separator decomposition representation in dynamic trees using asymptotically optimal labels. In order to maintain the short labels, the scheme uses relatively low message complexity. In particular, if the initial dynamic tree contains just the root, then the scheme incurs an O(log3n) amortized message complexity per topology change, where n is the current number of nodes in the tree. As a separator decomposition is a fundamental decomposition of trees used extensively as a component in many static graph algorithms, our dynamic scheme for separator decomposition may be used for constructing dynamic versions to these algorithms. The paper then shows how to use our dynamic separator decomposition to construct rather efficient labeling schemes on dynamic trees, using the same message complexity as our dynamic separator scheme. Specifically, we construct efficient routing schemes on dynamic trees, for both the designer and the adversary port models, which maintain optimal labels, up to a multiplicative factor of O(loglogn). In addition, it is shown how to use our dynamic separator decomposition scheme to construct dynamic labeling schemes supporting the ancestry and NCA relations using asymptotically optimal labels, as well as to extend a known result on dynamic distance labeling schemes. [ABSTRACT FROM AUTHOR]

The controlled system is an uncertain nonlinear differential-algebraic subsystem (DASs) in a large-scale system. The problem of robust stabilization for such class of uncertain nonlinear DASs is considered in this paper. The robust stabilization controller is proposed based on backstepping approach using two-layer Artificial Neural Networks (ANN) whose weights are updated on-line. The closed-loop error systems are uniformly ultimately bounded (UUB) and the error of convergence can be made arbitrarily small. Finally, using the design scheme proposed in this paper, a governor controller is designed for one synchronous generator in a multi-machine power systems. The simulation results demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper develops a new blind channel identification method in frequency domain. Oversampled signal has the property of spectral redundancy in frequency domain which is corresponding to the cyclostationarity property in time domain. This method exploits the cyclostationarity of oversampled signals to identify possibly non-minimum phase FIR channels. Unlike many existing methods, this method doesn't need EVD or SVD of correlation matrix. Several polynomials are constructed and zeros of channels are identified through seeking for common zeros of those polynomials. It is in the similar spirit of Tong's frequency approach, but this new algorithm is much simpler and computationally more efficient. A sufficient and necessary condition for channel identification is also provided in this paper. This condition is quite similar to Tong's time domain theory but it is derived from a novel point of view. [ABSTRACT FROM AUTHOR]

Most of the algorithms proposed in the literature deal with the problem of digital modulation classification and consider classic probabilistic or decision tree classifiers. In this paper, we compare and analyze the performance of 2 neural network classifiers and 3 support vector machine classifiers (i.e. 1-v-r type, 1-v-1 type and DAG type multi-class classifier). This paper also deals with the modulation classification problems of classifying both analog and digital modulation signals in military and civilian communications applications. A total of 7 statistical signal features are extracted and used to classify 9 modulation signals. It is known that the existing technology is able to classify reliably (accuracy ≥ 90%) only at SNR above 10dB when a large range of modulation types including both digital and analog is being considered. Numerical simulations were conducted to compare performance of classifiers. Results indicated an overall success rate of over 95% at the SNR of 10dB in all classifiers. Especially, it was shown that 3 support vector machine classifiers can achieve the probabilities of correct classification (Pcc) of 96.0%, 97.3% and 97.8% at the SNR of 5dB, respectively. [ABSTRACT FROM AUTHOR]

Recently, the distribution and using of the digital multimedia contents are easy by developing the internet application program and related technology. However, the digital signal is easily duplicated and the duplicates have the same quality compare with original digital signal. To solve this problem, there is the multimedia fingerprint which is studied for the protection of copyright. Fingerprinting scheme is a technique which supports copyright protection to track redistributors of electronic information using cryptographic techniques. Only regular user can know the inserted fingerprint data in fingerprinting schemes differ from a symmetric/asymmetric scheme and the scheme guarantee an anonymous before re-contributed data. In this paper, we present a new scheme which is the detection of colluded multimedia fingerprint by neural network. This proposed scheme is consists of the anti-collusion code generation and the neural network for the error correction. Anti-collusion code based on BIBD(Balanced Incomplete Block Design) was made 100% collusion code detection rate about the average linear collusion attack, and the Hopfield neural network using (n,k) code designing for the error bits correction confirmed that can correct error within 2bits. [ABSTRACT FROM AUTHOR]

Currently, there is little quantitative research on macroscopic real estate finance at home and abroad. Seen from the whole system of real estate finance, this paper chooses 14 main indexes to compose an evaluation index system. Based on the evaluation index system, an error -back-propagation BP network model is built to evaluate the growth of real estate finance. Data of real estate financial system from 1997-2005 are used as train and test samples of BP neural network. After training, the BP neural network is used to evaluate and forecast by simulation. Through the good accuracy of evaluation and forecasting, the model is proved to be very efficient. By comparing the growing difference of two adjacent years and analyzing the related macro financial policies in related years, the running effect of related real estate financial policies in related years is gained. So by using the evaluation model of this paper, decision makers can decide to use what kind of macro adjusting and controlling policies to gain anticipated aim of real estate finance in the future. [ABSTRACT FROM AUTHOR]

Code reuse in software reuse has several limitations such as difficulties of understanding and retrieval of the reuse code written by other developers. To overcome these problems, it should be possible to reuse the analysis/design information than source code itself. In this paper, I present analogical matching techniques for the reuse of object models and design patterns. We have suggested the design patterns as reusable components and the representation techniques to store them. The contents of the paper are as follows. 1) Analogical matching functions to retrieve analogous design patterns from reusable libraries. 2) The representation of reusable components to be stored in the library in order to support the analogical matching. [ABSTRACT FROM AUTHOR]

According to analysis of both neuro-electrophysiological experimental data and the biophysical properties of neurons, in early research paper we proposed a new biophysical model that reflects the property of energy coding in neuronal activity. On the based of the above research work, in this paper the proposed biophysical model can reproduce the membrane potentials and the depolarizing membrane current by means of neuro-electrophysiological experimental data. Combination with our previous research results, the proposed biophysical model is demonstrated again to be more effective compared with known biophysical models of neurons. [ABSTRACT FROM AUTHOR]

The existence and global asymptotic stability of a large class of Cohen-Grossberg neural networks is discussed in this paper. Previous papers always assume that the amplification function has positive lower and upper bounds, which excludes a large class of functions. In our paper, it is only needed that the amplification function is positive. Also, the model discussed is general, the method used is direct and the conditions needed are weak. [ABSTRACT FROM AUTHOR]

The paper investigates the existence and global stability of periodic solution of non-autonomous neural networks with delay. Then the existence and uniqueness of periodic solutions of the neural networks are discussed in the paper. Moreover, criterion on stability of periodic solutions of the neural networks is obtained by using matrix function inequality, and algorithm for the criterion on the neural networks is provided. Result in the paper generalizes and improves the result in the existing references. In the end, an illustrate example is given to verify our results. [ABSTRACT FROM AUTHOR]

This paper mainly discusses a generic scheme for on-line adaptive critic design for nonlinear system based on neural dynamic programming (NDP), more exactly, an improved action-depended dual heuristic dynamic programming (ADDHP) method. The principal merit of the proposed method is to avoid the model neural network which predicts the state of next time step, and only use current and previous states in the method, as makes the algorithm more suitable for real-time or on-line application for process control. In this paper, convergence proof of the method will also be given to guarantee the control to reach the optimal. At last, simulation result verifies the performance. [ABSTRACT FROM AUTHOR]

This paper discusses a new application of adaptive dynamic programming (ADP). Meanwhile, traffic control as an important factor in social development is a valuable research topic. Considering with advancement of ADP and importance of traffic control, this paper present a new signal control in a single intersection. Simulation results show that the proposed signal control is valid. [ABSTRACT FROM AUTHOR]

This paper presents a model predictive control (MPC) based on a neural network (NN) model for air/fuel ration (AFR) control of automotive engines. The novelty of the paper is that the severe nonlinearity of the engine dynamics are modelled by a NN to a high precision, and adaptation of the NN model can cope with system uncertainty and time varying effects. A single dimensional optimization algorithm is used in the paper to speed up the optimization so that it can be implemented to the engine fast dynamics. Simulations on a widely used mean value engine model (MVEM) demonstrate effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

This paper describes an efficient scheme of probabilistic packet marking. The main idea is to preserve the victims' IP addresses at the routers participating in the packet marking scheme, based on the precondition that a router won't begin to marking until it receives a signal from the victim. Then, the destination address field of IP header can be used to carry edge information without fragmenting, and the identification field can be used to check attack paths' validity under DDoS. We describe the scheme and discuss the number of packets required for reconstructing the attack paths, the number of false positives of attackers and the extra cost at routers in this paper. [ABSTRACT FROM AUTHOR]

This paper presents an improved framework for an existing adaptive method for scheduling divisible load. The proposed framework delimits in a new way the application field of the method. This method can be used to schedule parallel applications whose total workload is unknown a priori, and can deal as well with the unpredictable execution conditions commonly encountered on grids. Some parameters which quantify the dynamic nature of these varying execution conditions are discussed in the paper. Keywords: scheduling, divisible load, parallel application, grid, master-worker, on-line, multi-round, heterogeneity, dynamicity. [ABSTRACT FROM AUTHOR]

It is often believed that the performance of ant system, and in general of ant colony optimization algorithms, depends somehow on the scale of the problem instance at hand. The issue has been recently raised explicitly [1] and the hyper-cube framework has been proposed to eliminate this supposed dependency. In this paper, we show that although the internal state of ant system—that is, the pheromone matrix—depends on the scale of the problem instance under analysis, this does not affect the external behavior of the algorithm. In other words, for an appropriate initialization of the pheromone, the sequence of solutions obtained by ant system does not depend on the scale of the instance. As a second contribution, the paper introduces a straightforward variant of ant system in which also the pheromone matrix is independent of the scale of the problem instance under analysis. [ABSTRACT FROM AUTHOR]

In this paper, we present our ongoing work of a policy-driven approach to security requirements of grid data management systems (GDMS). We analyse the security functionalities of existing GDMS to determine their shortcomings that should be addressed in our work. We identify a comprehensive set of security requirements for GDMS followed by the presentation of our proposed Security Requirements Model. Derivation of security policies from security requirements and their consequent refinement is also presented in this paper. Our approach of addressing modelling issues by providing requirements for expressing security related quality of service is the key step to turn storage systems into knowledge representation systems. Keywords:Grid security, requirements analysis, distributed data management. [ABSTRACT FROM AUTHOR]

This paper considers the quantum query complexity of ε-biased oracles that return the correct value with probability only 1/2 + ε. In particular, we show a quantum algorithm to compute N-bit OR functions with $O(\sqrt{N}/{\varepsilon})$ queries to ε-biased oracles. This improves the known upper bound of $O(\sqrt{N}/{\varepsilon}^2)$ and matches the known lower bound; we answer the conjecture raised by the paper [1] affirmatively. We also show a quantum algorithm to cope with the situation in which we have no knowledge about the value of ε. This contrasts with the corresponding classical situation, where it is almost hopeless to achieve more than a constant success probability without knowing the value of ε. [ABSTRACT FROM AUTHOR]