Computational Efficiency

This paper introduces the dynamic neuro-fuzzy local modeling system (DNFLMS) that is based on a dynamic Takagi-Sugeno (TS) type fuzzy inference system with on-line and local learning algorithm for complex dynamic hydrological modeling tasks. Our DNFLMS is aimed to implement a fast training speed with the capability of on-line simulation where model adaptation occurs at the arrival of each new item of hydrological data. The DNFLMS applies an on-line, one-pass, training procedure to create and update fuzzy local models dynamically. The extended Kalman filtering algorithm is then implemented to optimize the parameters of the consequence part of each fuzzy model during the training phase. Local generalization in the DNFLMS is employed to optimize the parameters of each fuzzy model separately, region-by-region, using subsets of training data rather than all training data. The proposed DNFLMS is applied to develop a model to forecast the flow of Waikoropupu Springs, located in the Takaka Valley, South Island, New Zealand, and the influence of the operation of the 32 Megawatt Cobb hydropower station on spring flow. It is demonstrated that the proposed DNFLMS is superior in terms of model complexity and computational efficiency when compared with models that adopt global generalization such as a multi-layer perceptron (MLP) trained with the back propagation learning algorithm and the well-known adaptive neural-fuzzy system (ANFIS).

Abstract—The current proliferation of mobile devices has resulted in a large diversity of hardware specifications, each designed for different services and applications (e.g. cell phones, smart phones, PDAs). At the same time, e-mail message delivery has become a vital part of everyday communications. This article provides a cost-aware study of an RSA-based e-mail protocol executed upon the widely used Apple iPhone1,2 with ARM1176JZF-S, operating in an High Speed Downlink Packet Access (HSDPA) mobile environment. The proposed study employs formal analysis techniques, such as probabilistic model checking, and proceeds to a quantitative analysis of the e-mail protocol, taking into account computational parameters derived by the devices ’ specifications. The value of this study is to form a computer-aided framework which balances the tradeoff between gaining in security, using high-length RSA keys, and conserving CPU resources, due to hardware limitations of mobile devices. To the best...

The last decade has witnessed a rapid proliferation of superscalar cache-based microprocessors to build high-end capability and capacity computers primarily because of their generality, scalability, and cost effectiveness. However, the recent development of massively parallel vector systems is having a significant effect on the supercomputing landscape. In this paper, we compare the performance of the recentlyreleased Cray X1 vector system with that of the cacheless NEC SX-6 vector machine, and the superscalar cache-based IBM Power3 and Power4 architectures for scientific applications. Overall results demonstrate that the X1 is quite promising, but performance improvements are expected as the hardware, systems software, and numerical libraries mature. Code reengineering to effectively utilize the complex architecture may also lead to significant efficiency enhancements.

In this paper we analyze the current role of sketching in engineering design, and its importance as a learning outcome in engineering courses. Then we examine the current disconnection between sketching and CAD tools in the new product development (NPD) process, concluding that recent advances in the field of sketch-based interfaces and modelling (SBIM) promise better integration of those separated worlds. Sketching is certainly a powerful tool that greatly enhances design creativity and helps designers during the fixation of new products, but as a result of using computer-aided drafting in both engineering education and professional practice, hand-sketching skills have been overlooked. A recent survey conducted by the ASEE's Engineering Design Graphics Division includes the ability to sketch engineering objects in the freehand mode as the second main engineering student's outcome. This means that engineering teaching community considers sketching to be a capital outcome for current and future engineers. Hence, sketching must regain its capital role in engineering schools, but digital sketching must replace paper-and-pencil sketching, and it must become a "natural" process that does not disturb the user. It seems feasible now, since the reasons for its absence come only from the facts that appropriate hardware was not available until the recent advent of tablet PCs, and that currently available academic applications are not yet prepared for broad, commercial use. Moreover, the majority of handbooks and textbooks oriented towards assisting CAD users do not exploit the limited advantages that current CAD applications can offer during conceptual design. Such books introduce the reader to a world of actions (drawing) instead of a world of ideas (designing). In short, they are oriented towards obtaining maximum computer efficiency, instead of being designerefficiency oriented. In this context, commercially available CAD tools with some "pseudo-sketching" capabilities are far from being an alternative to satisfy sketching needs as they are clearly oriented towards detailed and not conceptual design, forcing designers precisely in the wrong direction. This process is better than the simplest and obsolete computer-aided drawing of predefined geometrical figures which was allowed by the first generation CAD tools. However, it is far from being genuine sketching; where incomplete and ill-defined geometries actually germinate from the designer's mind's eye. Current CAD applications are not suitable tools to integrate both conceptual design (where sketches dominate) and detailed design (where three-dimensional modelling is the nucleus). True computer-aided sketching (CAS) tools, oriented towards engineering design, are required. SBIM is an emerging research field, and seems to be able to create the computer tools required to shift to a new paradigm where sketches should be used as input to create digital engineering models in a true computer-aided ideation environment. In this paper, we analyze the main characteristics that a successful and fully integrated CAS tool must include for its application to engineering teaching.

Balancing computational efficiency with recognition accuracy is one of the major challenges in real-world video-based face recognition. A significant design decision for any such system is whether to process and use all possible faces detected over the video frames, or whether to select only a few 'best' faces. This paper presents a video face recognition system based on probabilistic Multi-Region Histograms to characterise performance trade-offs in: (i) selecting a subset of faces compared to using all faces, and (ii) combining information from all faces via clustering. Three face selection metrics are evaluated for choosing a subset: face detection confidence, random subset, and sequential selection. Experiments on the recently introduced MOBIO dataset indicate that the usage of all faces through clustering always outperformed selecting only a subset of faces. The experiments also show that the face selection metric based on face detection confidence generally provides better recognition performance than random or sequential sampling. Moreover, the optimal number of faces varies drastically across selection metric and subsets of MOBIO. Given the trade-offs between computational effort, recognition accuracy and robustness, it is recommended that face feature clustering would be most advantageous in batch processing (particularly for video-based watchlists), whereas face selection methods should be limited to applications with significant computational restrictions.

The primary focus of this paper is the enhancement of the computational efficiency of the body of revolution (BOR) scattering problem with a view to making it practical for solving large body problems. The problem of the electromagnetic scattering by a perfectly conducting BOR is considered, although the methods provided herein can be extended to multilayered dielectric bodies as well. Typically, the generation of the elements of the moment method matrix consumes a major portion of the computational time. In this paper it is shown how this time can be significantly reduced hy manipulating the expression for the matrix elements in a manner that allows one to compute them efficiently by using the fast Fourier transform (FFT). A new technique is also presented for extracting the singularity of the Green's function that appears within the integrands of the matrix diagonal, further enhancing the usefulness of the FFT. It is shown that, with the use of the method discussed herein, the computational time can be improved by at least an order of magnitude for large bodies in comparison to that for the existing algorithms.

This article introduces the finite state projection ͑FSP͒ method for use in the stochastic analysis of chemically reacting systems. One can describe the chemical populations of such systems with probability density vectors that evolve according to a set of linear ordinary differential equations known as the chemical master equation ͑CME͒. Unlike Monte Carlo methods such as the stochastic simulation algorithm ͑SSA͒ or leaping, the FSP directly solves or approximates the solution of the CME. If the CME describes a system that has a finite number of distinct population vectors, the FSP method provides an exact analytical solution. When an infinite or extremely large number of population variations is possible, the state space can be truncated, and the FSP method provides a certificate of accuracy for how closely the truncated space approximation matches the true solution. The proposed FSP algorithm systematically increases the projection space in order to meet prespecified tolerance in the total probability density error. For any system in which a sufficiently accurate FSP exists, the FSP algorithm is shown to converge in a finite number of steps. The FSP is utilized to solve two examples taken from the field of systems biology, and comparisons are made between the FSP, the SSA, and leaping algorithms. In both examples, the FSP outperforms the SSA in terms of accuracy as well as computational efficiency. Furthermore, due to very small molecular counts in these particular examples, the FSP also performs far more effectively than leaping methods.

Modern web search engines are expected to return the top-k results efficiently. Although many dynamic index pruning strategies have been proposed for efficient top-k computation, most of them are prone to ignoring some especially important factors in ranking functions, such as term-proximity (the distance relationship between query terms in a document). In our recent work [Zhu, M., Shi, S., Li,

Robust calibration of option valuation models to quoted option prices is non-trivial but crucial for good performance. A framework based on the state-space formulation of the option valuation model is introduced. Non-linear (Kalman) filters are needed to do inference since the models have latent variables (e.g. volatility). The statistical framework is made adaptive by introducing stochastic dynamics for the parameters. This allows the parameters to change over time, while treating the measurement noise in a statistically consistent way and using all data efficiently. The performance and computational efficiency of standard and iterated extended Kalman filters (EKF and IEKF) are investigated. These methods are compared to common calibration such as weighted least squares (WLS) and penalized weighted least squares (PWLS). A simulation study, using the Bates model, shows that the adaptive framework is capable of tracking time varying parameters and latent processes such as stochastic ...

This paper is a follow-up of preViouS work on optimal spotpricing of electricity [3]. The issue of long-term planning in the electric utility industry as it relates to the adoption of socially optimal spot pricing is considered. Optimal investment conditions are derived and an algorithm is developed which allows integration of these conditions into long-term planning. The proposed algorithm is an extensionl of Bootht-aleriaux probabilistic production costingand thus exhibits high computational efficiency. It provides the building block forincorporating spot pricing into a number of mathematical programminaf methodologies currently used in generation expansion planning studies. The algorithm's usefulness to produce optimal spot price forecasts that may induce socially efficient investments in non-utility-owned generation and to facilitate empirical studies on the income distributional impact of spot price implementation is also discussed.

A statistical discrete-time model is proposed for simulating wideband multiple-input multiple-output (MIMO) fading channels which are triply selective due to angle spread, Doppler spread, and delay spread. The new discrete-time MIMO channel model includes the combined effects of the transmit filter, physical MIMO multipath channel fading, and receive filter, and it has the same sampling period as that of the MIMO receiver. This leads to very efficient simulation of physical continuous-time MIMO channels. A new method is also presented to efficiently generate the MIMO channel stochastic coefficients. The statistical accuracy of the discrete-time MIMO channel model is rigorously verified through theoretical analysis and extensive simulations in different conditions. The high computational efficiency of the discrete-time MIMO channel model is illustrated by comparing it to that of the continuous-time MIMO channel model. The new model is further employed to evaluate the channel capacity of MIMO systems in a triply selective Rayleigh fading environment. The simulation results reveal some interesting effects of spatial correlations, multipaths, and number of antennas on the MIMO channel capacity. Index Terms-Discrete-time channel model, multiple-input multiple-output (MIMO) channel, multiple-input multiple-output multipath channel capacity, Rayleigh fading, triply selective fading, wide-sense stationary uncorrelated scattering (WSSUS) multipath channel. I. INTRODUCTION T HE multiple-input multiple-output (MIMO) communication architecture has recently emerged as a new paradigm for high data rate wireless cellular communications in rich multipath environments. Using multiple-element antenna arrays at both the transmitter and receiver, which effectively exploits the spatial dimension in addition to time and frequency dimensions, this architecture shows channel capacity potential far beyond that of traditional techniques. In quasi-static, independent and identically distributed (i.i.d.) frequency flat Rayleigh fading channels, the MIMO capacity scales linearly with the number of antennas under some conditions [1], [2]. However, in practice,

An efficient algorithm combining the adaptive integral method and the discrete complex-image method (DCIM) is presented in this paper for analyzing large-scale microstrip structures. The arbitrarily shaped microstrips are discretized using triangular elements with Rao-Wilton-Glisson basis functions. These basis functions are then projected onto a rectangular grid, which enables the calculation of the resultant matrix-vector product using the fast Fourier transform. The method retains the advantages of the well-known conjugate-gradient fast-Fourier-transform method, as well as the excellent modeling capability offered by triangular elements. The resulting algorithm has the memory requirement proportional to ( ) and the operation count for the matrix-vector multiplication proportional to ( log ), where denotes the number of unknowns. The required spatial Green's functions are computed efficiently using the DCIM, which further speeds up the algorithm. Numerical results for some microstrip circuits and a microstrip antenna array are presented to demonstrate the efficiency and accuracy of this method.

In this paper a new meta-heuristic optimisation technique is proposed. The method is based on the Parallel Tabu Search (PTS) algorithm and the application is the optimal electrical distribution systems reinforcement planning through the installation of photovoltaic plants, parallel cables, capacitor banks and transformers. The issue is a combinatorial optimisation problem; the objective function is a non-linear expression of a large number of variables. In these cases, meta-heuristics have proved to work well and one of the most efficient is the Tabu Search algorithm. For large-scale problems, parallelisation improves Tabu Search computational efficiency as well as its exploration ability. In this paper, an enhanced version of PTS, Evolutionary Parallel Tabu Search (EPTS), is proposed. It performs reproduction operators on subneighbourhoods directing the search towards more promising areas of the search space. The problem of distribution systems reinforcement planning has been studied in detail and the results of the application show that the EPTS outperforms the PTS and Particle Swarm Optimisation algorithms.

This paper presents a new scheme of face image feature extraction, namely, the two-dimensional Fisher linear discriminant. Experiments on the ORL and the UMIST face databases show that the new scheme outperforms the PCA and the conventional PCA + FLD schemes, not only in its computational efficiency, but also in its performance for the task of face recognition.

Page 1. FAST ACTIVE CONSTELLATION EXTENSION FOR MIMO-OFDM PAR REDUCTION Brian S. Krongold ARC Special Research Centre for Ultra-Broadband Information Networks Dept. of Electrical & Electronic Engineering University of Melbourne, Australia ...

Many hard algorithmic problems dealing with graphs, circuits, formulas and constraints admit polynomial-time upper bounds if the underlying graph has small treewidth. The same problems often encourage reducing the maximal degree of vertices to simplify theoretical arguments or address practical concerns. Such degree reduction can be performed through a sequence of splittings of vertices, resulting in an expansion of the

By making an example of the earlier proposed cyclic convolution algorithms, the computational efficiency of the multidimensional techniques over finite fields is investigated. It is shown that the multidimensional techniques are inferior to the directly designed algorithms for all lengths except when applied to lengths whose exponents are relatively prime. Relations between the complexities of the directly designed algorithms and those derived through the multidimensional techniques are also established in various cases.

Many areas of design involve both quantifiable and subjective goals, preferences, and constraints. Aesthetic and other subjective aspects of design are typically ignored in optimization models because they are difficult to model with mathematics; however, they are extremely important in areas such as product design and architectural design. This article presents an interactive method for integrating mathematical optimization with human decision-making during conceptual design of architectural floorplan layouts. The optimization models and algorithms were presented in a previous article. Here, an object-oriented representation allows the designer to interact with physically relevant building objects during optimization. The designer's interaction causes the program to dynamically change the optimization representation on-the-fly by adding, deleting, and modifying objectives, constraints, and structural units. This work presents mathematical optimization as a tool to assist the designer in refining ill-defined design problems during the early conceptual design phase. The designer can quickly explore design alternatives visually and computationally by taking advantage of computational algorithms to maintain feasibility and compute efficient solutions.

This paper presents a means for estimating parametric timing yield and guiding robust design-for-quality in the presence of manufacturing and operating environment variations. Dual emphasis is on computational eficiency and providing meaningful robust-design guidance. Computational eflciency is achieved by basing the proposed methodology on a post-processing step applied to the report generated as a by-product of static timing analysis. Eficiency is also ensured by exploiting the fact that for small processing/environment variations, a linear model is adequate for capturing the resulting delay change. Meaningful design guidance is achieved by analyzing the timing-related injluence of variations on a path-by-path basis, allowing designers pevorm a qualityoriented design pass focused on key paths. A coherent strategy is provided to handle both die-to-die and withindie variations.

An improved gradient-enhanced approach for softening elasto-plasticity is proposed, which in essence is fully nonlocal, i.e. an equivalent integral nonlocal format exists. The method utilises a nonlocal field variable in its constitutive framework, but in contrast to the integral models computes this nonlocal field with a gradient formulation. This formulation is considered 'implicit' in the sense that it strictly incorporates the higher-order gradients of the local field variable indirectly, unlike the common (explicit) gradient approaches. Furthermore, this implicit gradient formulation constitutes an additional partial differential equation (PDE) of the Helmholtz type, which is solved in a coupled fashion with the standard equilibrium condition. Such an approach is particularly advantageous since it combines the long-range interactions of an integral (nonlocal) model with the computational efficiency of a gradient formulation. Although these implicit gradient approaches have been successfully applied within damage mechanics, e.g. for quasi-brittle materials, the first attempts were deficient for plasticity. On the basis of a thorough comparison of the gradient-enhancements for plasticity and damage this paper rephrases the problem, which leads to a formulation that overcomes most reported problems. The two-dimensional finite element implementation for geometrically linear plain strain problems is presented. One-and two-dimensional numerical examples demonstrate the ability of this method to numerically model irreversible deformations, accompanied by the intense localisation of deformation and softening up to complete failure.

20 The Dynamically Dimensioned Search (DDS) continuous global optimization algorithm by 21 Tolson and Shoemaker [2007] is modified to solve discrete, single-objective, constrained Water 22 Distribution System (WDS) design problems. The new global optimization algorithm for WDS 23 optimization is called Hybrid Discrete Dynamically Dimensioned Search (HD-DDS) and 24 combines two local search heuristics with a discrete DDS search strategy adapted from the 25 continuous DDS algorithm. The main advantage of the HD-DDS algorithm compared with other 26

To model the impact dynamics of a golf drive, finite element (FE) models of the ball and the clubhead are created and combined to simulate the collision of the two bodies. A three-piece golf ball is modelled using only solid elements, while the clubhead is modelled using solid elements for the crucial area of the impact, i.e. the clubface, and using shell elements for the rest of the clubhead to improve the computational efficiency of the simulation. The correct transfer of forces and moments in the transition area between the shell and solid elements is assured by introducing kinematic nodal constraints using rigid elements. The FE model is used to optimize the shape of the clubface in three steps to maximize the launch velocity of the golf ball for central impacts. A final clubface shape is reached and a total improvement of 4.8 m/s over the initial design is obtained. This is a 7% gain in launch velocity, which results in a driving length advantage of approximately 20 m (or 22 yards) until the first contact with the ground. During the optimization process, the mechanical impedances of the two colliding bodies were recorded and compared. It is shown that the optimal clubhead geometry does not agree with the impedance matching theory by comparing the optimization results to those obtained from a simple lumped parameter model.

To support the sharing and reuse of formally represented knowledge among AI systems, it is useful to define the common vocabulary in which shared knowledge is represented. A specification of a representational vocabulary for a shared domain of discourse—definitions of classes, relations, functions, and other objects—is called an ontology. This paper describes a mechanism for defining ontologies that are portable over representation systems. Definitions written in a standard format for predicate calculus are translated by a system called Ontolingua into specialized representations, including frame-based systems as well as relational languages. This allows researchers to share and reuse ontologies, while retaining the computational benefits of specialized implementations.We discuss how the translation approach to portability addresses several technical problems. One problem is how to accommodate the stylistic and organizational differences among representations while preserving declarative content. Another is how to translate from a very expressive language into restricted languages, remaining system-independent while preserving the computational efficiency of implemented systems. We describe how these problems are addressed by basing Ontolingua itself on an ontology of domain-independent, representational idioms.

A new heuristic algorithm and new reduction techniques for the type 1 assembly line balancing problem are presented. The new heuristic is based on the well-known Hoffmann heuristic and builds solutions from both sides of the precedence network to choose the best. The reduction techniques aim at augmenting precedences, conjoining tasks and increasing operation times. The heuristic is tested on its own and also in combination with the reduction techniques. The tests, which are carried out on a well-known benchmark set of problem ...

This work introduces a new family of link-based dissimilarity measures between nodes of a weighted, directed, graph that generalizes both the shortest-path and the commute-time (or resistance) distances. This measure, called the randomized shortest-path (RSP) dissimilarity, depends on a parameter θ and has the interesting property of reducing, on one end, to the standard shortest-path distance when θ is large and, on the other end, to the commute-time distance when θ is small (near zero). Intuitively, it corresponds to the expected cost incurred by a random walker in order to reach a destination node from a starting node, while maintaining a constant entropy (related to θ) spread in the graph. The parameter θ is therefore biasing gradually the simple random walk on the graph towards the shortest-path policy. By adopting a statistical physics approach and computing a sum over all the possible paths, it is shown that the RSP dissimilarity from every node to a particular node of interest can be computed efficiently by solving two linear systems of n equations, where n is the number of nodes. On the other hand, the dissimilarity between every couple of nodes is obtained by inverting an n × n matrix. The proposed measure could be used for various graph mining tasks such as computing betweenness centrality, finding dense communities, etc, as shown in the experimental section.

In this paper we investigate how to sequence surgical cases in a day-care facility. We specify a multicriteria objective function in which we minimize the peak use of recovery beds, the occurrence of recovery overtime and the violation of various patient and surgeon preferences. The limited availability of resources and the occurrence of medical precautions, such as an additional cleaning of the operating room after the surgery of an infected patient, are taken into account. We apply column generation to solve this combinatorial optimization problem and propose a dynamic programming algorithm to solve the pricing problem. The computational efficiency of this dynamic programming approach is validated through comparison with a mixed integer linear programming approach. In order to obtain integer variables, we embed the column generation loop in an enumerative branch-and-price framework. We elaborate on various branching strategies and branching schemes and examine their impact on the solution quality. The test instances for the computational experiments are generated using real-life data of the surgical day-care center at the academic hospital UZ Leuven Campus Gasthuisberg (Belgium).

This paper addresses a scheduling problem motivated by scheduling of diffusion operations in the wafer fabrication facility. In the target problem, jobs arrive at the batch machines at different time instants, and only jobs belonging to the same family can be processed together. Parallel batch machine scheduling typically consists of three types of decisions-batch forming, machine assignment, and batch sequencing. We propose a memetic algorithm with a new genome encoding scheme to search for the optimal or near-optimal batch formation and batch sequence simultaneously. Machine assignment is resolved in the proposed decoding scheme. Crossover and mutation operators suitable for the proposed encoding scheme are also devised. Through the experiment with 4860 problem instances of various characteristics including the number of machines, the number of jobs, and so on, the proposed algorithm demonstrates its advantages over a recently proposed benchmark algorithm in terms of both solution quality and computational efficiency.

In this paper the recently developed "Floating Search" algorithms are presented and modified to a more compact form facilitating their direct comparison with the well known (11 r ) search. The properties ofthe Floating Search methods are investigated, especially with respect to their tolerance t o nonmonotonic criteria. Their computational efficiency is demonstrated b y results on real data of high dimensionality.

This work presents two new methods for the calculation of node signal probabilities in combinational networks that improve the state of the art by providing a better accuracy than existing algorithms and a deeper insight in the effects of first order correlations due to multiple fan-out reconvergencies. The proposed algorithms are shown to favorable compare with existing procedures in the analysis of significant benchmarks.

In this paper, we present new algorithms for the calculation of convolutions by means of number theoretic transforms over modulo rings. Not only are these algorithms more efficient than currently used methods, but they are also very flexible. Indeed, using special algorithms for short convolutions allows trading computational efficiency for structural simplicity.

Simulating the complex fashion garments of haute couture can only be reached through an optimal combination of modeling techniques and numerical methods that combines high computation efficiency with the versatility required for simulating intricate garment designs. Here we describe optimal choices illustrated by their integration into a design and simulation tool that allow interactive prototyping of garments along drape motion and comfortability tests on animated postures. These techniques have been successfully used to bring haute couture garments from early draping of fashion designers, to be simulated and visualized in the virtual world.

A spectral stochastic finite element formulation with consideration of multi-layer effect and spatial variability of material properties is developed for probabilistic analysis of laminated composite plates. The material properties of each lamina are modeled as a set of random fields and represented by the Karhunen-Loève expansion. The expansion is incorporated into a spatial discretization in accordance with a standard finite element procedure based on the first-order shear deformation theory. A spectral expansion with use of polynomial chaos is employed to represent the stochastic nodal displacements in terms of standard normal random variables. A preconditioning matrix is then proposed for the solution of spectral stochastic finite element equations with use of a preconditioning conjugate gradient technique. The various statistics of interest of the system responses are finally obtained by means of the coefficients of the spectral expansion. The numerical accuracy and the computational efficiency of the method are demonstrated by comparison with Monte-Carlo simulation.

The use of a radar altimeter is to measure the height of the reflecting surface when the instrument passes overhead. A delay/Doppler altimeter (DDA) reduces the along-track resolution cell using correlation of pulses within burst, as in a synthetic aperture radar, with the net effect of synthesizing an array with a narrower lobe. The processing of DDA data therefore implies the correction of the phase delay of pulses, which is said to be Doppler beam sharpening since it can be reduced to a DFT in the along-track direction. The noise term on Doppler echoes is decreased by incoherent summing of contributions of different bursts. Summation of these contributions requires that pointing of each beam is directed toward a given output grid of samples on ground, and this can be a nontrivial task, depending on the topography profile. In this letter, a comparison between different beam sharpening algorithms is presented: Two of them are similar in their basic idea even if with different implementations; the third one can be considered as reference since no approximations are needed in principle, but is computationally inefficient. What results is that the first couple can be considered basically equivalent in terms of precision and computational efficiency, with little differences at the change of the environmental conditions. Results based on simulated data of simple and complex scenarios are presented as support of the reasoning.

Two families of derivative free two-point iterative methods for solving nonlinear equations are constructed. These methods use a suitable parametric function and an arbitrary real parameter. It is proved that the first family has the convergence order four requiring only three function evaluations per iteration. In this way it is demonstrated that the proposed family without memory supports the Kung–Traub hypothesis (1974) on the upper bound 2n of the order of multipoint methods based on n + 1 function evaluations. Further acceleration of the convergence rate is attained by varying a free parameter from step to step using information available from the previous step. This approach leads to a family of two-step self-accelerating methods with memory whose order of convergence is at least 2+5≈4.236 and even 2+6≈4.449 in special cases. The increase of convergence order is attained without any additional calculations so that the family of methods with memory possesses a very high computational efficiency. Numerical examples are included to demonstrate exceptional convergence speed of the proposed methods using only few function evaluations.

The scan-line corrector (SLC) of the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor failed in 2003, resulting in about 22% of the pixels per scene not being scanned. The SLC failure has seriously limited the scientific applications of ETM+ data. While there have been a number of methods developed to fill in the data gaps, each method has shortcomings, especially for heterogeneous landscapes. Based on the assumption that the same-class neighboring pixels around the un-scanned pixels have similar spectral characteristics, and that these neighboring and un-scanned pixels exhibit similar patterns of spectral differences between dates, we developed a simple and effective method to interpolate the values of the pixels within the gaps. We refer to this method as the Neighborhood Similar Pixel Interpolator (NSPI). Simulated and actual SLC-off ETM+ images were used to assess the performance of the NSPI. Results indicate that NSPI can restore the value of un-scanned pixels very accurately, and that it works especially well in heterogeneous regions. In addition, it can work well even if there is a relatively long time interval or significant spectral changes between the input and target image. The filled images appear reasonably spatially continuous without obvious striping patterns. Supervised classification using the maximum likelihood algorithm was done on both gap-filled simulated SLC-off data and the original "gap free" data set, and it was found that classification results, including accuracies, were very comparable. This indicates that gap-filled products generated by NSPI will have relevance to the user community for various land cover applications. In addition, the simple principle and high computational efficiency of NSPI will enable processing large volumes of SLC-off ETM+ data.

This article addresses the problem of managing Moving Objects Databases (MODs) which capture the inherent imprecision of the information about the moving object's location at a given time. We deal systematically with the issues of constructing and representing the trajectories of moving objects and querying the MOD. We propose to model an uncertain trajectory as a three-dimensional (3D) cylindrical body and we introduce a set of novel but natural spatio-temporal operators which capture the uncertainty and are used to express spatio-temporal range queries. We devise and analyze algorithms for processing the operators and demonstrate that the model incorporates the uncertainty in a manner which enables efficient querying, thus striking a balance between the modeling power and computational efficiency.

In this paper we introduce the interlaced chirp Z transform (Interlaced CZT). It is based on the computation of several carefully staggered CZT that are progressively interlaced to result in a spectrum that has denser frequency samples where needed. This simple modification of the CZT is shown to result in significant computational savings over the regular CZT, as well as the zoom CZT (ZCZT). The CZT computes uniformly spaced frequency samples over a desired range with an efficiency similar to that of the FFT algorithm. In practice, several CZT's over increasingly smaller ranges are required to obtain denser frequency samples where needed. This process is referred to as the ZCZT. The interlaced CZT improves on the ZCZT by interlacing successive CZT's such that, in each step, the previous samples are included with the new ones, resulting in dense frequency sampling with increased computational efficiency. The regular CZT is also compared to the ZCZT based on a typical regimen and the conditions under which either is superior are elucidated.

The paper considers a current example of Wächter and Biegler which is shown not to converge for the standard primal-dual interior-point method for nonlinear programming. Three possible solutions to the problem are derived and discussed, including shifting slack variables, a trust region method, and a modified barrier method. The paper then compares LOQO, a line-search interior-point code, with SNOPT, a sequential-quadraticprogramming code, and NITRO, a trust-region interior-point code on a large test set of nonlinear programming problems. Specific types of problems which can cause LOQO to fail are identified.

This paper presents a new method of SPICE macromodeling of magnetic components, that considers both the magnetic saturation and hysteresis processes, as well as the selfheating electrothermal effects. It uses the Analog Behavioral Modeling facilities of modern SPICE like simulators, that enable the description of a large number of nonlinear algebraic and differential equatiotos by means of nonlinear controlled voltage and current sources. Thus, the electromagnetic and electrothermal laws are described with "in line equation" controlled sources, that form an electrical equivalent circuit of the global magnetic-electrical-thermal behavior of the magnetic component. The B-H lo'op is piece-wise-linear approximated and therefore any kind of magnetic material can be modeled. The temperature and fre:quency dependencies of the B-H loop are directly introduced in the macromodel with "look-up table" controlled sources. The resulting model is portable in all the modern SPICE simulators that support the ABM facilities, and it assures a higher aocuracy and a better computational efficiency, with no coiovergence problems. The proposed behavioral macromodel was tested for several commercial magnetic cores, showing a good agreement with data sheets characteristics. 0-7803-4340-9/981$10.00 0 1998 IEEE. 393