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Multi-layer neural networks have been used in this paper for modeling nonlinear behaviour of concrete gravity dams under earthquake excitation. Koyna dam which has been studied extensively by other authors in the past has been studied as test example in this paper too, where the nonlinear response of its crest has been modelled by the proposed algorithm. The main steps of the algorithm are as follows: First the concrete gravity dam has been numerically analyzed for its nonlinear behaviour under earthquake excitation to generate numerical data to be used in the training of the neural networks. To this end the dam has been subjected to a white noise excitation so that the generated data could be rich enough for the training of a general neuro-modeller of the dam response. The neuro-modeller has then been trained on the generated data to learn the hysteretic behaviour of the dam implicitly. Then the neural network has been tested on a number of earthquakes including near field as well as very strong earthquakes for verification. The results obtained in this study prove that the method has been successful regarding the generalization capabilities of the trained neuro-modeller where other earthquakes than those used in its training have been used in its testing. In the tests, the neuro-modeller could predict the response with high precision. One significant benefit of using this algorithm is in cases where it is desired to use collected data from tests on experimental models or through monitoring of the response of a dam to prepare a suitable model for predicting its response under any earthquake. Another benefit is the time of analysis which can be reduced by this method. Once the neuro-modeller is trained, it can predict the response of the dam to any earthquake without the need to be updated. [ABSTRACT FROM AUTHOR]

This paper presents an alternative energy-based algorithm to provide speech/silence classification. The algorithm is capable to track non-stationary signals and dynamically calculate instantaneous value for threshold using adaptive scaling parameter. It is based on the observation of a noise power estimation used for computation of the threshold can be obtained using minimum and maximum values of a short-term energy estimate. The paper presents this novel voice activity detection algorithm, its performance, its limitations, and some other techniques which deal with energy estimation as well. [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]

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]

This paper presents an important outcome of a research programme which focuses on the development of a method for synthesizing, under controlled conditions in the laboratory, the non-stationary random vibrations generated by road transport vehicles. It addresses an important limitation of current methods used for synthesising random vehicle vibrations which assume that vibrations produced by wheeled vehicles can be approximated by a zero-mean, normally-distributed (Gaussian) random process and, therefore, fails to accurately reproduce the fluctuations in vibration levels that occur naturally during road transportation realizations [1]. The paper builds upon the observation that non-stationary random vehicle vibrations are composed of a sequence of zero-mean random Gaussian processes of varying standard deviations [2]. It discusses the important parameters that need to be addressed when dealing with the synthesis of random sequences. The paper presents the development of a change-point detection algorithm that was used to determine the length of stationary segments within a large number of typical non-stationary random vibration records. These include measured vibration records as well as numerically-generated records based on measured pavement profiles. The algorithm, based on the cum-sum / bootstrapping techniques, was developed and applied to the instantaneous magnitude of sample vibration records. The statistical distribution of segment lengths for each vibration record was computed with the aim of identifying similarities and trends for the development of an overall statistical model for segment lengths to be used for synthesis purposes. One outcome of note was that the shape of the segment length distributions computed from a wide range of vibration records are generally comparable and exhibit an asymptotic-like decrease in probability of occurrence as the segment length increases. This behaviour was found to be adequately modelled with a hyperbolic trigonometric function which was found to be useful for characterising the general statistical behaviour of segment length for non-stationary random vibrations produced by road vehicles. Finally, the significance and relevance of this outcome with respect to the synthesis of non-stationary vibrations for package evaluation and validation purposes is highlighted. [ABSTRACT FROM AUTHOR]

This paper presents one of the outcomes of a research project concerned with the development of a method for synthesizing, under controlled conditions in the laboratory, the random vibrations generated by road transport vehicles. It addresses some of the deficiencies and limitations of current random vibration synthesis methods used for evaluating and validating the performance of packaging systems. The paper deals with the development of a technique for decomposing non-stationary random vibration signals into constituent Gaussian elements. The hypothesis that random non-stationary vehicle vibrations are essentially composed of a sequence of zero-mean random Gaussian processes of varying standard deviations is tested and the paper reveals that the variations in the magnitude of the vibrations are the cause of the leptokurtic, non-Gaussian nature of the process. It is shown how non-stationary vibration signals can be systematically decomposed into these independent random Gaussian elements by means of a numerical curve-fitting procedure. The paper describes the development of the algorithm which is designed to automatically extract the parameters of each constituent Gaussian process namely the RMS level and the Vibration Dose. The validity of the Random Gaussian Sequence Decomposition (RGSD) method was tested using a set of road vehicle vibration records and was found to be capable of successfully extract the Gaussian estimates as well as the corresponding Vibration Doses. Validation was achieved by comparing the sum of these Gaussian estimates against the PDF of the original vibration record. All validation cases studied show that the RGSD algorithm is very successful in breaking-down non-stationary random vibration records into their constituent Gaussian processes. Finally, the significance and relevance of this technique with respect to the synthesis of non-stationary vibrations for package evaluation and validation purposes is highlighted [ABSTRACT FROM AUTHOR]

The objective of this paper is to study how algorithms of optimization affect the parametersestimation of Autoregressive AR(1)Models. In our research we have represented the AR(1) models in linear state space form and applied the Kalman Filters to estimate the different unknown parameters of the model. Many methods have been proposed by researchers for the estimation of the parameters in the case of the linear state space models. In our work we have emphasized on the estimation through the Maximum Likelihood (ML). Statisticians have used many algorithms to optimise the likelihood function and they have proposed many filters; publishing their results in many papers. In spite of the fact that this field is so extended, we have emphasized our study in the financial field. Two quasi-Newton algorithms: Berndt, Hall, Hall, and Hausman (BHHH) and Broyden-Fletcher-Goldfarb-Shanno (BFGS), and the Expectation-Maximization (EM) algorithm have been chosen for this study. A practical study of these algorithms applied to the maximization of likelihood by means of the Kalman Filter have been done. The results are focused on efficiency in time of computation and precision of the unknown parameters estimation. A simulation study has been carried out, using as true values the parameters of this model published in the literature, in order to test the efficiency and precision of our implemented algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a combined model approach of Fuzzy Logic and Genetic Algorithm applied for non-linear behavioral compensation of Silicon Controlled Rectifier (SCR), for its improved performance (optimal variable output voltage). The optimized parametric compensation of SCR will be done by amalgamated algorithm of Fuzzy Logic Control and Genetic Algorithm. It is a shift from existing practice of Fuzzy Logic based control /compensation, as reported in the literature. In this work, a Fuzzy Logic based optimal control system has been developed for input voltage regulation of SCR, which is further optimized by Genetic Algorithm. The input voltage regulation of SCR is needed to meet the varying load current demand in various industrial applications of the device. The proposed scheme as presented in this paper leads to the optimal regulation of input voltage for SCR. The results have shown a remarkable reduction in the error which was otherwise existing in the device and its application circuit. The accuracy level at the output of the SCR after the implementation of the proposed amalgamated algorithm is ranging between 99.0 to 99.5%. It also suits the nonlinearly varying load current requirement for a given industrial system employing SCR. [ABSTRACT FROM AUTHOR]

This paper presents an application of fuzzy logic theory to the reconstruction of solid models from engineering drawings. In engineering drawing, two-dimensional (2D) orthographic projections represent an object ambiguously, it requires a numerous amount of combinatorial searches in the process of reconstruction of three-dimensional (3D) drawing. This paper proposes an algorithm which applies fuzzy logic to identify the category of the object in order to implement the further operations. Once an object has been classified to be either rotational or prismatic the major operation of either revolve or extrude will be executed correspondingly to generate the 3D solid model. Compared with earlier approaches, the present method focuses on ambiguous issues which improve the efficiency of the reconstruction process. A program has been compiled to implement the present algorithm which has proved to be very practicable. [ABSTRACT FROM AUTHOR]

Springback is a very important factor to influence the quality of sheet metal forming. Accurate prediction and controlling of springback is essential for the design of tools for sheet metal forming. Several approaches have been proposed for springback compensation by modification of the tooling shape. These approaches are iterative finite element methods. In this paper an analytical approach is presented for one step modification of the tooling shape in channel forming process to compensate the springback error. With the help of this approach, the optimum die shape for producing the target shape can be obtained in a few seconds. The algorithm of springback compensation by inverse FE modelling is also presented. The results of the analytical approach coincide with those of FE approach. The accuracy of the obtained results is verified by the experimental results and high precision is achieved. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of computing tail conditional expectations (TCE) or portfolio gains at specific times (T) in the future. We present efficient algorithms to handle the following two cases: (1) we have only one option (call or put) in our portfolio, denoted as a single-stock-single-option portfolio (SSSO); (2) we have a stock and some of its options in our portfolio, denoted as a single-stock-multiple-option portfolio (SSMO). Compared with previous simulation algorithms, our algorithms compute TCE of a given portfolio more efficiently and still maintain the same degree of accuracy. In the SSSO case, we reduce the computational time complexity from the SSSO-Naïve Algorithm's O(m*n) to SSSO algorithm's O(m+n), where m is the number of possible price outcomes for an underlying stock at time T and n is the number of possible price outcomes for an underlying stock at time U (maturity) in respect to each possible price outcome for an underlying stock at time T. In the SSMO case, we provide two algorithms to compute the TCE. The computational time complexity of the SSMO-Naive Algorithm is O(q*m*n+m*log(m)), where q is the number of options. The computational time complexities of our two algorithms are O(q*(m+n)+m*log(m)), and O(q*log(q)+n+m*q+ m*q*log(q)+m*log(m)) or O(q*log(q)+n+m*q+m*q*log(m)+m* log(m)). In both cases, our experiments show that when m and n are greater than five thousand, our algorithms run thousands of times faster than the naive algorithms. [ABSTRACT FROM AUTHOR]

In this paper an algorithm for minimization of a nondifferentiable function is presented. The algorithm uses the Moreau-Yoshida regularization of the objective function and its second order Dini upper directional derivative. It is proved that the algorithm is well defined, as well as the convergence of the sequence of points generated by the algorithm to an optimal point. An estimate of the rate of convergence is given, too. [ABSTRACT FROM AUTHOR]

This paper is concerned with adaptive numerical methods for initial value problems governed by systems of ordinary differential equations. Here we introduce a novel step selection algorithm based on the simple idea that locally all continuous functions can be suitably approximated by a straight line. Finally we present two sample numerical computations performed by our step selection algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we consider two finite element model updating problem which incorporate the measured modal data into the analytical finite element model, producing an adjusted model on the damping and stiffness, that closely match the experimental modal data. We develop an efficient numerical algorithm for solving this problem. The new algorithm is direct methods which require O(nk²) flops. Here n is the dimension of the coefficient matrices defining the analytical model and k is the number of measured eigenpairs. [ABSTRACT FROM AUTHOR]

Since the evaluation of each individual is based on the time-consuming structural analysis, the computational efficiency of truss topology optimization using genetic algorithm is very low. The paper focuses on this challenging problem. It is observed that there are a number of duplicate individuals appearing repetitively in the evolutionary process. Therefore, an individual identification technique is introduced to avoid evaluating the duplicate individuals by the time-consuming structural analysis but by searching the evolutionary history data to save computing time, the computational complexity of this technique is deduced. The results of two truss examples verify that the technique can effectively improve the efficiency of the algorithm. Based on this identification technique, numeric experiments are implemented to study the influence of several factors, i.e., the population size, the max generation, and the scale of problems, on the proportion of duplicate individuals. Results show that the population size has a significant impact on the proportion, and that both the max generation and the scale of problems have little influence. [ABSTRACT FROM AUTHOR]

The widespread popularity of mobile computing devices, such as Laptops, handheld devices and cell phones, as well as recent advances in the wireless communication technologies have motivated researchers to provide novel solutions and applications for the users that were previously not feasible. The users of these mobile computing devices expect the same features and services from these devices as were previously available from conventional desktop computers. However to provide mobility and reduction of size of these mobile devices, the battery life is a major concern; several hardware based techniques have been proposed which results in more energy efficient systems as compared to the earlier systems. Even after these hardware improvement based techniques the problem still persists and it is believed that software based techniques have enough potential to reduce the energy demand to overcome the problems faced due to energy limitation. In this paper, we look into the problem of distributing the computational tasks among different devices in hybrid network environment. By hybrid networks we mean a network containing both wired as well as wireless handheld devices. The reason of selecting hybrid network environment is because most of the applications of mobile devices require accessing resources on the high bandwidth unlimited energy devices connected on wired network to help conserve the energy utilization of the energy limited wireless handheld devices. We have proposed a novel energy-aware scheduling algorithm to solve the problems of resource constrained mobile devices. Our scheduling algorithm schedules a set of computational tasks which may have operational and communication dependencies, into the set of heterogeneous devices so as to minimize both the energy consumption and time taken by the tasks to be completed. Experiments show that significant improvement in the over all performance in terms of energy consumption and execution time of the handheld devices can be achieved by using our algorithm. [ABSTRACT FROM AUTHOR]

The continued advancement of software-defined radio (SDR) technology has been a key factor in furthering research about the implementation of most signal processing algorithms at baseband. Traditionally, most algorithms have been carried out at radio frequency (RF). With the coming of SDR, the processing can be done at baseband frequencies which are more compatible with the fast developing software radio technology. This paper looks at matched filter detection and investigates the possibility and benefits of carrying out the detection process at quadrature baseband (QBB). A simple chirp signal is considered for the analysis. The analysis is carried out using MatLab simulations at RF and QBB and the results do show the possibility of carrying out the detection process at QBB with the expected benefits as compared to carrying out the process at RF. [ABSTRACT FROM AUTHOR]

Emergence of modern techniques for scientific data collection has resulted in large scale accumulation of data pertaining to diverse fields. Conventional database querying methods are inadequate to extract useful information from huge data banks. Cluster analysis is one of the major data analysis methods and the k-means clustering algorithm is widely used for many practical applications. But the original k-means algorithm is computationally expensive and the quality of the resulting clusters heavily depends on the selection of initial centroids. Several methods have been proposed in the literature for improving the performance of the k-means clustering algorithm. This paper proposes a method for making the algorithm more effective and efficient, so as to get better clustering with reduced complexity. [ABSTRACT FROM AUTHOR]

Line Spectrum Frequencies (LSF) have been the current parameter set to represent LPC coefficients in speech coding. Extensive research has been performed to exploit their interframe and intraframe correlations and quantize them more efficiently. Interframe coding of LSF's can cause error propagation when frame erasures occur. Since most LSF quantizers were designed with the primary concerns of bit-rate and complexity, less attention was paid to error propagation. In this paper, we consider the erasure performance of LSF differential scalar quantizer (DSQ) and compare it with the interframe coding method embedded in the standard G723.1 of the ITU. Our results show that with only 5% extra bit-rate, DSQ algorithm is much more robust to frame erasures and improvements in terms of spectral distortion and Enhanced modified bark spectral distortion (EMBSD) tests under various packet loss conditions are obtained. [ABSTRACT FROM AUTHOR]

An ad hoc network (MANET) has no fixed networking infrastructure, and consists of mobile nodes that communicate with each other. Since nodes are mobile, Routing in ad hoc network is a challenging task. Efficient routing protocols can make better performance in such networks. Many protocols have been proposed for ad hoc networks which the most common types are: Ad hoc on-demand Distance Vector (AODV), Dynamic Source Routing (DSR), Optimized Link State Routing (OLSR). In this paper, we introduce a new Position and Neighborhood based Routing (PNR) algorithm for mobile ad hoc networks which uses GPS and new algorithm to reduce the overhead caused by position update messages. We also compare our scheme with DSR, AODV, OLSR for two metrics: packet delivery ratio and end-to-end delay. We use GlomoSim [1] to evaluate these protocols. [ABSTRACT FROM AUTHOR]

Autonomous navigation in unknown cluttered environments is one of the main challenges for search and rescue robots inside collapsed buildings. Being able to compare different search strategies in various search fields is crucial to attain fast victim localization. Thus we discuss an algorithmic development and proliferation of realistic after--disaster test fields for search and rescue simulated robots. In this paper we characterized our developed search environments by their fractal dimensions. This index has shown to be a discriminative index for narrow pathways inside confined and cluttered spaces in our simulation test fields. In this approach a simulation of challenging parts of NIST red course is constructed and a benchmark for search strategies has been evaluated. [ABSTRACT FROM AUTHOR]

Maintenance of the cache consistency is a complicated issue in the wireless mobile environment. Caching of frequently accessed data items on the node can reduce the bandwidth requirement of the mobile node environment. In this paper, we present a new technique called Modified Energy Efficient Cache Invalidation Algorithm (MEECIA) especially to reduce the uplink bandwidth consumption in the wireless ad-hoc network, by choosing following modes: Slow, Fast, Super-fast. The mode is selected based on threshold specified for time and the number of node requesting the updated data to other node. Simulation results demonstrate that our algorithm is efficient in improving mobile caching, reducing the communication bandwidth and also efficiently utilizing the energy in ad-hoc network. Compared to the previously reported cache based Invalidation Report (IR), our scheme can significantly improve the performance in terms of energy consumption and reduce the query latency in ad-hoc network. [ABSTRACT FROM AUTHOR]

In this paper we propose an approach to improve the performance of existing flood based routing algorithm Ad-hoc on demand Distance Vector Routing [2] for mobile Ad-hoc wireless networks based on heuristic searching technique. Flooding technique is often used for route discovery in on demand mechanism in MANET such as AODV, DSR. To avoid the problem of wireless broadcast storm, the heuristic searching approach was introduced in the process of finding route from source node to destination node. Heuristic function considers the characteristics of MANET (Bandwidth, number of nodes in the given range). If a node S wants to send a packet to D in the flooding method S sends packet to all its neighbor nodes, but in the proposed scheme tries to reduce broadcasting by finding heuristic measures of the neighbors of S. The heuristic measures are evaluated by applying a function to all neighbors. [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]

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]

This paper proposes an age artificial immune system (AAIS), for optimal order pickings in an Automated Storage and Retrieval System (AS/RS) with multiple input/ output stations. A mathematical model is presented to describe the characteristics of the AS/RS. It is optimized with the proposed algorithm, which is based on the clonal selection principle and the aging concept. Unlike conventional algorithms for artificial immune systems, the proposed algorithm consists of antibodies whose abilities to be cloned and to survive depend on their ages, and adopts a mutation scheme based on randomized rankings. To further improve the performance of AAIS, a crossover operator is also included in the algorithm to form the AAIS-CX algorithm. The performance of both algorithms is tested with the problems of optimal order pickings in an AS/RS with multiple input/output stations. Comparison of the results obtained by using AAIS-CX, AAIS, the techniques of nearest neighbor heuristics, genetic algorithms and ant colony systems clearly shows that AAIS-CX is superior to the other algorithms. Suggestions for future work are also included [ABSTRACT FROM AUTHOR]

In this paper a new approach to optimize nuclear power plant designs based on global risk reduction are described. In design the focus is on as components quality as redundancy levels. Meanwhile in maintenance and test tasks the focus is on as scheduling tasks as human reliability. The models based on probabilistic risk analysis are used to evaluate several designs and schedules proposed by an hybrid genetic algorithm. The best alternative is chosen to minimize the economical risk of down the production or of have an accident for all reasons considered. This approach has resulted in a new methodology to assure the risk for complex industrial systems too in a global way. So, it is possible considering several aspects such as component qualities, redundancy levels, task schedules for maintenance or tests tasks, and reliability human as a whole. [ABSTRACT FROM AUTHOR]

Texture analysis plays an increasingly important role in computer vision. Since the textural properties of images appear to carry useful information for discrimination purposes, it is important to develop significant features for texture. Various texture feature extraction methods include those based on gray-level values, transforms, auto correlation etc. We have chosen the Gray Level Co occurrence Matrix (GLCM) method for extraction of feature values. Image segmentation is another important problem and occurs frequently in many image processing applications. Although, a number of algorithms exist for this purpose, methods that use the Expectation-Maximization (EM) algorithm are gaining a growing interest. The main feature of this algorithm is that it is capable of estimating the parameters of mixture distribution. This paper presents a novel unsupervised segmentation method based on EM algorithm in which the analysis is applied on vector data rather than the gray level value. [ABSTRACT FROM AUTHOR]

Applications of the Particle Swarm Optimization (PSO) to solve image processing problem with a reference to a new automatic enhancement technique based on real-coded particle swarms is proposed in this paper. The enhancement process is a non-linear optimization problem with several constraints. The objective of the proposed PSO is to maximize an objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The feasibility of the proposed method is demonstrated and compared with Genetic Algorithms (GAs) based image enhancement technique. The obtained results indicate that the proposed PSO yields better results in terms of both the maximization of the number of pixels in the edges and the adopted objective evaluation. Computational time is also relatively small in the PSO case compared to the GA case. [ABSTRACT FROM AUTHOR]

The build up of water ice on aircraft flight surfaces poses a significant safety risk. As a result, much effort has gone into studying this problem in order to understand how individual droplets contribute to the accretion process. One approach has been to capture the moment of impact of a supercooled droplet onto a surface placed in an icing tunnel. However, this produces a large number of images that must be analysed manually. This paper describes the development of an automated analysis system, employing image processing techniques, that is capable of classifying the impact images without operator input. Using a carefully chosen feature vector and K-means clustering algorithm, the classification results from the automated system are comparable with that achieved using the manual approach. [ABSTRACT FROM AUTHOR]

This paper deals with different techniques for registration and fusion of remote sensed images. In this work the lower spatial resolution multispectral and higher resolution panchromatic images of SPOT satellite are used. These images are registered using a registration algorithm that combines a simple yet powerful search strategy based on stochastic gradient with the similarity measure as mutual information, together with a wavelet-based multi-resolution pyramid. The algorithm is found to give sub pixel registration accuracy. The study is limited to pairs of images, which are misaligned by rotation and/or translation. The registered images are subjected to a pixel level multispectral image fusion process using wavelet transform approach. Spectral quality assessments shows that compared to other conventional image fusion techniques, this fusion process using wavelet transform keeps much of the spectral information in the merged image with respect to the original multispectral one. Finally, segmentation is performed on the fused images to validate the algorithms used for registration and fusion and the results show better accuracy for wavelet based methods than the conventional methods. [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]

Linear equations provide useful tools for understanding the behavior of a wide variety of phenomena- from science and engineering to social sciences. A number of techniques have arisen to find the solution of these systems; examples are Jacobi, Gauss-Seidel, Successive Over Relaxation, and Multigrid. In this paper, we present an accelerated version of the Jacobi algorithm by implementing it on reconfigurable hardware devices- Field Programmable Gate Arrays such as Virtex II Pro, Altera Stratix and Spartan3L. The design presented is implemented using Handel-C, a hardware compiler. The implementation results obtained are compared with a software version results written in C++ and running on a general purpose processor. Final results illustrate that Jacobi on a reconfigurable hardware can outperform a software version of the same algorithm. [ABSTRACT FROM AUTHOR]

Partial Differential Equations (PDEs) play an essential role in modeling real world problems. The broad field of modeling such systems has drawn the researchers' attention for designing efficient algorithms for solving PDEs. Multigrid solvers have been shown to be the fastest due to its high convergence rate which is independent of the problem size. Many attempts have been made to exploit the inherent parallelism of these solvers. Yet, most efforts fail in this respect due to many factors (time, resources) governed by software implementations. In this paper, we present a hardware implementation of the V-cycle Multigrid method for finding the solution of a 2D-Poisson equation. We use Handel-C to implement our hardware design, which we map onto available Field Programmable Gate Arrays (FPGAs). We analyze the implementation performance using the FPGA vendor's tools. We compare our findings with a C++ version of the algorithm. The obtained results show better performance when compared to existing software versions. [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 a detailed comparison between a conventional PI controller and a variable structure controller based on a fuzzy sliding mode strategy used for speed control in direct torque control induction motor drive. Genetic algorithms are used to tune the PI controller gains to ensure optimal performance. The performance of the two controllers are investigated and compared for different dynamic operating conditions such as of reference speed and for load torque step changes at nominal parameters and in the presence of parameter variation and imprecision. Results show that the PI controller has better performance for nominal operating conditions while the fuzzy sliding mode is more robust against parameter variation and uncertainty, and is less sensitive to external load torque disturbances with a fast dynamic response. [ABSTRACT FROM AUTHOR]

In this paper, hybrid particle swarm optimization (PSO) is proposed for solving the challenging multi-contingency transient stability constrained optimal power flow (MC-TSCOPF) problem. The objective of this nonlinear optimization problem is to minimize the total fuel cost of the system and at the same time fulfil the transient stability requirements. The optimal power flow (OPF) with transient stability constraints considered is re-formulated as an extended OPF with additional rotor angle inequality constraints, which is suitable for hybrid PSO to solve. Comparison between various existing hybrid PSO techniques is carried out by solving the New England 39-bus system. Experimental results indicate that the hybrid PSO integrated with the mutation operation of genetic algorithms is better than the other existing hybrid PSO methods in both solution quality and stability. [ABSTRACT FROM AUTHOR]

In recent years, various algorithms for mining closed frequent itemsets (CFI) have been proposed. Different structures such as prefix sharing are used within these algorithms. However the name challenging problem in many of them is the high requirement of memory, especially in case of sparse datasets. Thus, most of the proposed methods are proper for dense datasets. In this paper we present a new approach to mining closed frequent itemsets using two structures, namely Patricia tree and PTArray. By using these two structures, both the response time and also memory consumption are reduced, significantly. The proposed method, called PTclose is suitable for both dense and sparse datasets. The algorithm is assessed throughout a set of experiments. The results narrate for the relative efficiency of the algorithm compared to other existing methods. [ABSTRACT FROM AUTHOR]

The research efforts of the DECIDE Research Group have resulted in a decision tool capable of handling imprecise information in complex decision situations. Some of the research has been directed towards developing decision analytical algorithms and applying these algorithms in a graphical user interface. The decision tool takes intervals as well as comparative relations as input constraint, and is incorporating sensitivity analyses into the representation, instead of applying separate sensitivity analyses on top of the evaluation procedure. However, besides the built-in sensitivity analysis, a second form of sensitivity analysis could be useful in order to point out the most critical probabilities, values, or weights to the decision at hand. This paper deals with the problems and the implementation of interval tornado diagrams in a decision tool supporting interval probabilities, values, criteria weights, as well as comparative relations. [ABSTRACT FROM AUTHOR]

Crossbars are main components of communication switches used to construct interconnection networks. Scheduling algorithm controls contention in switch architecture. Several scheduling algorithms were proposed for input-queued crossbar switch architectures. This paper suggests a Dynamic Scheduling Algorithm (DSA). This algorithm changes the priority rotation dynamically based on two parameters: queue occupancy and quality of service of input and output. DSA efficiently utilizes the buffers and at the same time gives good service to the selected inputs and outputs. The simulation results show that DSA saves loss of the cells due to buffer overflow and thus increases the throughput by 2% to 4% compared to its counter part. DSA reduces the latency for prescribed Quality of Service class input output and increases the average latency. [ABSTRACT FROM AUTHOR]

This paper presents an energy back-propagation algorithm (EBP). Learning and convergence processes of the standard backpropagation algorithm (SBP) are based on the energy function. The energy function is used with the convergence process to extract the nearest image for the unknown tested image. The EBP algorithm shows considerably better performance in terms of time of learning, time of convergence, and size of input image compared to the SBP algorithm. [ABSTRACT FROM AUTHOR]

In this paper incomplete quantitative data has been dealt by using the concept of fuzzy entropy. Fuzzy entropy has been used to extrapolate the data pertaining to the compressor current. Certain attributes related to the compressor current have been considered. Test data of compressor current used in this knowledge discovery algorithm knows the entire attribute clearly. The developed algorithm is very effective and can be used in the various application related to knowledge discovery and machine learning. The developed knowledge discovery algorithm using fuzzy entropy has been tested on a multi-compressor system for incomplete compressor current data and it is found that the error level is merely ± 4.40%, which is far better than other available knowledge discovery algorithms. [ABSTRACT FROM AUTHOR]

In this paper we used genetic algorithms to 1 find the solution of game theory. We proposed new method foe solving game theory and find the optimal strategy for player A or player B. We can benefit from the relationship between game theory and the linear programming to find the fitness function and tested this fitness function at different examples . [ABSTRACT FROM AUTHOR]

In order to be successful in multi-agent electronic negotiating environments, intelligent agents should be capable of adapting their negotiation strategies and tactics so that they can achieve an agreement with optimized profit. In this paper, some findings are going to be shown in which negotiating intelligent agents in electronic commerce start negotiating using a simplified standard protocol in conjunction with a combined negotiation. Taking advantage of a new developed evolutionary algorithm, agents configure their negotiation strategies somehow they can get more profit. They can use fuzzy fairness function to behavior with fairness or without fairness. [ABSTRACT FROM AUTHOR]

In this paper, a Neuro-Predictive (NP) controller is designed and implemented on a highly no-linear system, a model helicopter in a constrained situation. It is observed that the closed loop system with the NP controller has a significant overshoot and a long setting time in comparison to the same system with an existing fuzzy controller. In order to improve the undesired system performance, s Sugeno-type fuzzy compensator, having only two rules, is added to the control loop to adjust control input. The newly designed Neuro-Predictive control with Fuzzy Compensator (NPFC) improves the system performance in both overshoot and settling time. Furthermore, it is shown that the NPFC controlled system is robust to disturbance and parameter changes. [ABSTRACT FROM AUTHOR]