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This paper describes the study of cryptographic hash functions, one of the most important classes of primitives used in recent techniques in cryptography. The main aim is the development of recent crypt analysis hash function. We present different approaches to defining security properties more formally and present basic attack on hash function. The Main aim of this paper is the development of recent techniques applicable to crypt Analysis hash function, mainly from SHA family. Recent proposed attacks an MD5 & SHA motivate a new hash function design. It is designed not only to have higher security but also to be faster than SHA-256. The performance of the new hash function is at least 30% better than that of SHA-256 in software. And it is secure against any known cryptographic attacks on hash functions. [ABSTRACT FROM AUTHOR]

Moving object detection is very important for video surveillance. In this paper, we present a new real time motion detection algorithm that is based on the integration of accumulative optical flow and double background filtering method (long-term background and short-term background) to achieve better performance. The accumulative optical flow method is used to obtain and keep a stable background image to cope with variations on environmental changing conditions and the double background filtering method is used to eliminate the background information and separate the moving object from it. The biggest advantage of this algorithm is that it does not need to learn the background model from hundreds of images and can handle quick image variations without prior knowledge about the object size and shape. The algorithm has high capability of anti-interference and preserves high accurate rate detection at the same time. The effectiveness of the proposed algorithm for motion detection is demonstrated in a simulation environment and the evaluation results are reported in this paper. [ABSTRACT FROM AUTHOR]

In this paper, a recently developed fiber tracking algorithm to be used with diffusion tensor (DT) fields acquired via magnetic resonance imaging (MRI) is improved and applied to real brain DT-MR images. The method performs satisfactorily in regions where branching and crossing fibers exist and offers the capability of reporting a probability value for the computed tracts. This certainty figure takes into account both the anisotropy and the information provided by all the eigenvectors and eigenvalues of the diffusion matrix at each voxel. In previous papers of the authors, a simpler algorithm was applied only to elementary synthetic DT-MR images. As now presented, this algorithm is now adequately used with more intricate synthetic images and is applied to real white matter DT-MR images with successful results. A novel neural network is used to estimate the main parameters of the algorithm. Numerical experiments show a performance gain over previous approaches, specially with respect to convergence and computational load. The tracking of white matter fibers in the human brain will improve the diagnosis and treatment of many neuronal diseases. [ABSTRACT FROM AUTHOR]

In-vehicle Infotainment systems and the applications are becoming complex artifacts due to advancement in technology and increased competition. The performance of infotainment applications and the acceptance of these systems largely depend on the performance of Human Machine Interface software infrastructure. This paper presents our attempt to apply Model Driven Architecture (MDA) technology to generate the performance model for kernel of Human Machine Interface Framework for Infotainment (iHMIFw) applications. This paper proposes the performance modeling of iHMIFw by means of Unified Modeling Language (UML) State Machines and extended UML Profile for Schedulability, Performance, and Time. For the resulting performance model a transformation into a Generalized Stochastic Petri Net is established. [ABSTRACT FROM AUTHOR]

Automata theory has proved to be a cornerstone in theoretical computer science since last couple of decades. It is playing an important role in modeling behavior of complex systems. The algebraic automaton which is an advanced form of automata, having properties and structures from algebraic theory, is emerging with several modern applications. Optimization of logic based programs, design and development of model checkers are couple of examples of it. Design of a complex system not only requires the functionality but it also needs to capture its control behavior. Z notation is an ideal specification language used for describing state space of a system and operations over it. Consequently, an integration of algebraic automata and Z will be an effective tool for modeling purposes. In this paper, we have established a relationship between few fundamentals of algebraic automata and Z notation. At first, some important concepts of automata are transformed to Z notation. Then, we have given a formal construction of algebraic automata. Next, fundamental concepts of algebraic automata, for example, monoid, semi-group and group are formalized and refined. Finally, an important notion of homomorphism for verifying similarity between algebraic structures is described. Formal specification of this linkage is analyzed and validated using Z/EVES tool. [ABSTRACT FROM AUTHOR]

In recent past, there seems to have an increased demand for improvements in current Enterprise Resource Planning (ERP) solutions. One potential area is enhancing existing master and transaction data models in ERP. This paper presents an integrated approach to represent both master and transaction data, which in turn helps improving applications within ERP system environment. Master and transaction data are modeled using unitary structures, integrating individual data elements and structures and associated functions. Functional tasks of transaction data are incorporated at structural level for improved planning and scheduling of components, such as component allocation and goods receipt processes in the production order cycle. Further, enhancement of master and transaction data can allow for forward planning of many components involved in various functional applications in ERP. Thus, potential improvements include simultaneous planning of many components (materials, resources, operations and project activities), forward planning across many applications and finite loading of resources as part of overall planning. The implementation of unitary structures in an ERP system can be carried out using additional links between existing data models. Once developed and implemented in an ERP system, data and information are visible and transactions associated with them are more flexible. [ABSTRACT FROM AUTHOR]

The recent boom in wireless communications has led to a wide range of new applications. Wireless positioning is an emerging technology which can provide accurate locations for indoor environments when satellite based positioning systems are not available. In this paper, a Session Initiation Protocol (SIP) based system architecture for wireless positioning is described and an overview of how this can be used in overall system design is provided. New simulation results show that SIP is effective as a network signaling protocol for indoor wireless positioning. [ABSTRACT FROM AUTHOR]

Our work explores the use of several text categorization techniques for classification of manufacturing quality defect and service shop data sets into fixed categories. Although our work was in the area of manufacturing quality the technique is applicable to free form, short text summaries of data that may be stored in a database, file, or document. We refer to these types of text as "technical text passages". These summaries may not follow standard grammar conventions; they commonly contain abbreviations, technical phrases, misspelled words and industry specific acronyms. Typical types of text to be classified include aircraft engine repair shop findings, industrial manufacturing quality problems and corrective actions, and standardization of attributes in a bill-of-materials. In this paper, we will present our results in using machine learning and rule based algorithms to categorize text. Our results show that the rules based approach is as good as several machine learning approaches. For example, using Support Vector Machine algorithms we were able to achieve 82% accuracy on validation set, using 1,645 training samples and 823 validation samples. Each category had 50 or more samples. Using rule-based approach we were able to achieve 80% accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach to Arabic letter recognition is proposed. The system is based on the classified vector quantization (CVQ) technique employing the minimum distance classifier. To prove the robustness of the CVQ system, its performance is compared to that of a standard artificial neural network (ANN)-based solution. In the CVQ system, each input letter is mapped to its class using the minimum Euclidean distance. Simulation results are provided and show that the CVQ system always produces a lower Mean Squared Error (MSE) and higher success rates than the current ANN solutions. [ABSTRACT FROM AUTHOR]

Pose problem is a big challenge for applying face recognition technology under real world conditions. In this paper, appearance based approach was proposed to recognize face across front and non-frontal view images by reconstructing frontal view features. Statistical learning method based on sample images is applied to find transformation matrix which encapsulated general knowledge of pose transition in feature subspace, therefore, different view feature vectors constituted linear equations and transformation matrix can be solved from the equations by least square (LS) approach. Experimental results on popular FERET and CMU databases showed that the proposed method could cope with the head rotation roughly within half profile view. Compared with model based approaches, this method is not dependent on heavy computation and has merit of easy implementing in live conditions. [ABSTRACT FROM AUTHOR]

In this paper, we attempt to develop image watermarking algorithms which are portable to a variety of applications such as copyright protection, fingerprinting and identification. Therefore, we require that the watermark be binary and be not only detectable but also extractable. The embedding technique is based on the parent-child structure of the multiwavelet transform called "triple tree" and this technique does not require the original image in the watermark extraction. The experimental results show that the watermark. [ABSTRACT FROM AUTHOR]

This paper presents a new method for the signature analysis of mechanical watch movements. Contrary to the existing method, it analyzes the time-frequency features of mechanical watch movement through a combination of reassigned scalogram and Finite Element Analysis (FEA). By mapping the signal into a 2D domain of time and frequency, the reassigned scalogram reveals the frequency components of the shocks at different time. On the other hand, FEA gives the modal frequencies of the movements. By comparing the frequency components at different shocks to the modal frequencies of the movement, possible malfunctions of the movement can then be detected. A number of demonstration examples are included. [ABSTRACT FROM AUTHOR]

This paper presents a multi step-size monic normalization equation-error linear filter. We also extend the idea to nonlinear adaptive filter and derive a multi step-size monic normalization equation-error bilinear filter (MSS MNEEBF). The algorithms enjoy fast convergence behavior and can remove biased estimates associated with conventional equation-error adaptive filters. Simulation results validate the usefulness of our algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes two models of adding relations to a linking pin type organization structure where every pair of siblings in a complete K-ary tree of height H is adjacent: (i) a model of adding an edge between two nodes with the same depth N and (ii) a model of adding edges between every pair of nodes with the same depth N. For each of the two models, an optimal depth N* is obtained by maximizing the total shortening path length which is the sum of shortening lengths of shortest paths between every pair of all nodes. [ABSTRACT FROM AUTHOR]

This paper proposes a new discrete-time queue analytical model based on BLUE algorithm in order to determine the network congestion in preliminary stages. We compare the original BLUE, which has been implemented in Java, with our proposed analytical model with regards to different performance measures (average queue length, throughput, average queueing delay and packet loss probability). The comparison results show that the proposed discrete-time queue analytical model outperforms BLUE algorithm in terms of throughput and packet loss probability. Moreover, the proposed model maintains the throughput performance regardless whether the amount of the traffic load is light or heavy. Furthermore, we calculate the packet dropping probability function for our analytical model and the BLUE algorithm in order to decide which algorithm drops fewer packets. [ABSTRACT FROM AUTHOR]

This paper presents an experimental study for evaluting the decision support value if queueing network (QN) based simulation models for information system design performance. For illustration, queueing network simulation models have been extracted corresponding to three annotated design alternatives of a selected case study. The design alternatives are produced using logical requirements of the selected system. The performance of each alternative is then predicted using quantifiable parameters considering the dynamics of the system such as service time, waiting time and number of entities waiting in the system. In particular, results have shown that the first alternative performs better than the other two in terms of the selected parameters. In general, the case study revealed that QN-based simulation models alternatives in terms of selected parameters and under given assumptions. This is also means that the use of simulation may lead ti better designed information systems. [ABSTRACT FROM AUTHOR]

Cartesian Ad hoc Routing Protocols (CARP) are a set of three adaptive and connectionless protocols that address the problem of routing and power consumption in MANET. These protocols are used to limit the number of forwarding nodes in a logical transmission area. Density of an ad hoc network considered a network parameter in CARP, and is used in the calculations to form a logical transmission area. The CARP assume that the nodes have some knowledge about the density from previous activity the network, but no specific density determining mechanism has been described. This research paper presents a density determining mechanism based on traffic monitoring. The CARP, many of the other ad hoc protocols, geneate a great deal of traffic for implementing some form of flooding, and this traffic can be exploited to deternibe density. The nodes in a network with implementation of CARP can use this density value to form the logical broadcast area. As the nodes in MANET are potentially in motion, density may change over time. The proposed algorithm also tracks the density value over time and keeps it updated as per situation of the network. The simulation results in OPNET Modeler 10.5 validate the performance of this density determining algorithm for CARP. [ABSTRACT FROM AUTHOR]

ACPhssrt is a hybrid process algebra obtained by extending a combination of two existing extensions of Algebra of Communicating Processes (ACP), namely the process algebra with continuous relative timing and the process algebra with propositional signals, for the specification and analysis of hybrid systems. In addition to equational axioms, this hybrid process algebra has rules to derive equations with the help of real analysis. ACPhssrt is also closely related to the theory of hybrid automata, so ACPhssrt can be reasonable easily translated to hybrid automata. This enables automatic verification of ACPhssrt specifications using existing hybrid automaton-based verification tools (e.g. PHAVer and HyTech). Since ACPhssrt is a well-developed algebraic theory from the field of hybrid process algebras with the above-mentioned features, it seems to allow rigorous specification and analysis of digital/analog/mixed-signal circuits. In order to explore this fact, in this paper, we study a mixed-signal circuit: a half wave rectifier in ACPhssrt. Some basic properties of the half wave rectifier are analysed in a formal way and some correctness requirements are also proven to be satisfied. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the effectiveness of a financial time-series forecasting strategy which exploits the multi-resolution property of the wavelet transform. A financial series is decomposed into an over complete, shift invariant scale-related representation. In transform space, each individual wavelet series is modeled by a separate universal approximator in order to better utilize the detailed information in the lower scales of wavelet coefficients (high frequencies) and general (trend) information in the higher scales of wavelet coefficients (low frequencies). To form the overall forecast, the individual forecasts are then recombined by the linear reconstruction property of the inverse transform. Combining wavelet decomposition and universal approximators to financial time-series in our work shows promising performance. Our results indicate that wavelet preprocessing improves forecast accuracy for financial time series that exhibit variance changes and other complex local behavior. [ABSTRACT FROM AUTHOR]

We had previously proposed a rule-extraction method from computer log files using Automatically Defined Groups. In this method, agents search multiple rules cooperatively based on the difference between normal state log files and abnormal state log files. The more frequently used and the more reliable error-detection rule is, the more agents are allotted to search for the rules. Therefore, the number of agents for each rule can represent the importance of the rule. However, when the rule-extraction method is applied to the large-scale log files, which may contain a number of rules, a problematic situation on the number of agents could be observed. In the previous proposed method, the number of agents is fixed, so the number of agents may be lack for evaluating the each rule's importance minutely. In this paper, we propose an improved method, where the number of agents is adaptively increased depending on the number of discovered rules. As a result, by increasing the number of agents, the importance of respective rules could be evaluated minutely. In addition, the proposed method could acquire more rules than those by the method with the fixed number of agents. [ABSTRACT FROM AUTHOR]

Threshold group signature is an important kind of digital signature. But almost all the proposed schemes can not resist the collusive attack. This paper proposes a new threshold group signature scheme in which we randomly select an integer e associated with a group member and make V (x) including the information of e, so that collusive attackers are not able to forge a valid group signature of other members because of the lack of e. So our scheme not only has the properties of threshold group signature, but also can resist the collusive attack. [ABSTRACT FROM AUTHOR]