Total Computational Cost Research Articles

AN EFFICIENT BLOCK MATCHING ALGORITHM FOR FAST MOTION ESTIMATION USING COMBINED THREE STEP SEARCH AND DIAMOND SEARCH ALGORITHM

Recent day Communication between ends is facilitated by the development in the area of wired and wireless networks. So many researches are going on the field of motion estimation to achieve the requirement of recent day technology. Block matching algorithms are very useful in achieving the efficient and acceptable motion estimation. Total computation cost and bit budgeting can be efficiently controlled by properly modifying block matching algorithms. Video coding using Orthogonal Logarithmic Search Algorithm (OSA) is one of the best block matching algorithms used now days. Motion estimation is the most time consuming operation in a typical video encoder. This paper presents a novel method by combining modified three step (M3SS) algorithm with a different search pattern and diamond search (DS) based on dead cell for the block based motion estimation. Result shows that from the original Three step Search (3SS/TSS ) method, the PSNR value has increased and the computations time has been reduced effectively by modified 3SS and again to increase the search space we combined diamond search(DS) algorithm. The experimental results based on different video frames were compared to demonstrate the advantages of proposed fast motion estimation algorithm.

AN EFFICIENT THREE STEP ALGORITHM FOR FAST MOTION ESTIMATION IN MOBILE DOMAIN

The goal of this work is to find a fast method for motion estimation and motion segmentation using proposed model. Recent day Communication between ends is facilitated by the development in the area of wired and wireless networks. Block matching algorithms are very useful in achieving the efficient and acceptable compression. Block matching algorithm defines the total computation cost and effective bit budget. Recently fast search algorithm for video coding using Orthogonal Logarithmic Search Algorithm (OSA) has been proposed. Motion estimation is the most time consuming operation in a typical video encoder. This paper presents a novel method using three step algorithm with modified search pattern based on dead cell for the block based motion estimation. It has been found that from the original Three step Search (3SS/TSS ) method, the PSNR value has increased and the computations and thus computation time (faster) has been reduced significantly .The experimental results based on the number of video sequences were presented to demonstrate the advantages of proposed motion estimation technique.

Risk efficiency of irrigation to cereals in northeast Germany with respect to nitrogen fertilizer

The potential role of irrigation of cereals as a response to climate change is under debate. Especially under temperate continental conditions empirical evidence of crop yield response to irrigation in interaction with nitrogen fertilizer supply is rare. Besides mean yield effects, irrigation reduces yield variance, which may be an incentive for farmers to use irrigation. This paper investigates the risk-efficiency of irrigation in cereal production in a temperate continental climate, based on data from a long term field experiment on a sandy soil. Irrigation and no irrigation of winter rye (Secale cereale) and winter barley (Hordeum vulgare) were investigated in three different nitrogen (N) fertilizer levels. Crop yield response data (1995–2010) to irrigation and N fertilizer were used to calculate net returns, certainty equivalents (CE) for different levels of risk aversion and the conditional value at risk (CVaR) as a downside risk indicator in two price scenarios. The scenarios were calculated with a total cost and a partial budget approach. Irrigation was found to be profit-maximizing in all partial budget calculations, which sometimes required higher N input to be profit-maximizing. Irrigation and N fertilizer reduction were identified as risk mitigation strategies, even though their impact was limited. Irrigation reduced the downside risk only in the partial budget calculations. The analysis based on the CE did not show improved risk efficiency with irrigated management options. In contrast, reduced fertilizer input proved to be risk efficient at specific levels of risk aversion. The price expectations of winter rye and winter barley had a much higher impact on the ranking of the management options than risk aversion based on the crop yield variances. At low crop prices for all levels of risk aversion, irrigation of winter barley and winter rye was only economically justified if fixed costs for irrigation were not taken into account. At high crop prices, irrigation of winter barley was also justified based on the total cost calculation. However, this advantage was only given at a very low level of risk aversion. With increasing levels of risk aversion irrigation was not efficient based on the CE in the total cost accounting scenario. In conclusion, irrigation of cereals can contribute to downside risk mitigation and increased profits, if fixed costs for irrigation are covered. However, this conclusion holds only when irrigation is combined with an increased N intensity. If total costs need to be accounted for, irrigation in cereals is not an appropriate risk reduction strategy and a reduction of N input is more effective.

Addressing the curse of dimensionality in SSFEM using the dependence of eigenvalues in KL expansion on domain size

This work is aimed at reducing the dimensionality in the spectral stochastic finite element method (SSFEM)–thus the computational cost–through a domain decomposition (DD) method. This reduction hinges on some new mathematical results on domain size dependence of the Karhunen–Loève (KL) expansion. It has been reported in the literature that for few covariance kernels a lower domain size leads to a faster convergence of the KL eigenvalues. This observation leads to an early truncation of the KL expansion, and this reduction in stochastic dimensionality brings down the total computational cost. In this work first we mathematically show the generalization of this faster convergence, that is, for any arbitrary covariance kernel. This is achieved via developing a bound on eigenvalues as a function of the domain size. Then we prove that for a chosen number of terms in the KL expansion with any kernel for a one-dimensional process, the approximation error in the trace norm reduces with the domain size. Based on this domain size dependence, we propose an algorithm for solving a stochastic elliptic equation in a DD framework. The computational cost gain is demonstrated by a numerical study and is observed that the serial implementation of the proposed algorithm is about an order of magnitude faster compared to the existing method. The cost saving increases with the stochastic dimensionality in the global domain. The structure of this algorithm provides scope for parallelization, which would help in efficiently solving large scale problems. The sharpness of the proposed eigenvalue bounds is also tested for Gaussian and exponential kernels. The generalization opens avenues for developing further DD based SSFEM solvers.

Concept Development and Technology Choices for the More-connect Pilot Energy Renovation of Three Apartment Blocks in Denmark

In the context of the European Horizon2020 project MORE-CONNECT on prefab deep energy renovation of dwellings pilot demonstration projects are carried out in 6 of the participating countries. In Denmark the pilot project comprises a comprehensive energy renovation of three building blocks situated in Roskilde - southwest of Copenhagen. An energy renovation concept for these buildings has been developed according to a methodology developed for the MORE-CONNECT project aiming towards a Zero Energy Building (ZEB) level. The energy requirement for heating purposes is therefore to be brought down from around 100 kWh/m2 to below 20 kWh/m2 and a part of the electricity consumption to be produced by PV. The methodology calls for a calculation of total costs versus reduced Global Warming Potential (GWP) and primary energy consumption. The developed concept includes additional insulation in the external walls and roof, new 3-layer low-energy windows, a heat recovery ventilation system and renewable energy supply. Within the MORE-CONNECT two technologies are under development in Denmark: A new PV-roof solution in which the PV-cells are fully integrated in the roofing elements. This PV-roof can be fitted into any roof surface shape and if justified by the cost-optimisation it can be constructed as a PVT-roof connected to a heat-pump for heating purposes. The second technology under development is 3-D printing of a layer of façade insulation by an industry robot. The concept development follows first a path using conventionally available technologies and at the end the two new technologies are integrated in the concept and the corresponding traditionally technologies removed. The end result is an energy renovation reaching plus-energy level at competitive costs.

RISK MANAGEMENT METHODOLOGY OF INVESTMENT PROJECTS WITH ENVIRONMENTAL IMPACT

In projects, a strategic component is to identify all risks that might influence their success, prevent and manage them effectively. Risk management is essential to add value to an investment and improve results. With the economic and financial crisis in recent years, increasingly, more companies have realized the importance of using a system of risk management, given the multitude of variables that can influence the success of a project. These include: legislative changes, global political and economic instability, natural disasters, climate change, human resources, liquidity risk, environmental impact, risk of erroneous calculation of total project costs, risk of failure into initial project schedule, prolongation risk, the risk of failure of internal rate of return (IRR) and net present value (NPV) etc. The study proposes a new method of risk assessment and management in the investments projects with environmental impact and its application in a case study. The method will take into consideration besides the economic and financial indicators, hard to be understood by smaller investors, a set of simple and important questions that they have to analyze before deciding to start an investment project. The new methodology will have a comprehensive approach from identifying the investment opportunity, writing investment project until its implementation. This will take into account macroeconomic variables and the microeconomic environment that can influence the success of project implementation. The main advantage of applying the new methodology are: knowing all the variables that influence the success of a project realization; awareness of the importance of applying an effective risks management related to investment projects with environmental impact, increasing the quality and success of projects.

The Methodology for Calculation of Indicators Related to Passenger Trains of Unchanged Composition

Using the example of railway direction Moscow–Penza the article provides economic justification for the use of passenger trains of unchanged composition in growing competition environment in the transport market. At the same time the role of strengthening customer loyalty in attracting additional passenger flow and the value of proposed methodological tools that provide the calculation of total costs for organization of trains with rigid train scheme, as well as revenue and payback indicators of capital investment are estimated.

Quantum Monte Carlo with very large multideterminant wavefunctions.

An algorithm to compute efficiently the first two derivatives of (very) large multideterminant wavefunctions for quantum Monte Carlo calculations is presented. The calculation of determinants and their derivatives is performed using the Sherman-Morrison formula for updating the inverse Slater matrix. An improved implementation based on the reduction of the number of column substitutions and on a very efficient implementation of the calculation of the scalar products involved is presented. It is emphasized that multideterminant expansions contain in general a large number of identical spin-specific determinants: for typical configuration interaction-type wavefunctions the number of unique spin-specific determinants Ndetσ ( σ=↑,↓) with a non-negligible weight in the expansion is of order O(Ndet). We show that a careful implementation of the calculation of the Ndet -dependent contributions can make this step negligible enough so that in practice the algorithm scales as the total number of unique spin-specific determinants, Ndet↑+Ndet↓, over a wide range of total number of determinants (here, Ndet up to about one million), thus greatly reducing the total computational cost. Finally, a new truncation scheme for the multideterminant expansion is proposed so that larger expansions can be considered without increasing the computational time. The algorithm is illustrated with all-electron fixed-node diffusion Monte Carlo calculations of the total energy of the chlorine atom. Calculations using a trial wavefunction including about 750,000 determinants with a computational increase of ∼400 compared to a single-determinant calculation are shown to be feasible. © 2016 Wiley Periodicals, Inc.

Provably secure anonymous authentication with batch verification for mobile roaming services

Recently, multiple secure authentication schemes with user anonymity feature for mobile roaming services have been proposed to address general user privacy concern from mobile device users. As mobile devices usually have limited computing resources, it is difficult to develop an anonymous authentication scheme which possesses performance efficiency and security robustness at the same time. In this paper, a new anonymous authentication scheme for mobile roaming services is proposed to support fast authentication and preserve security strength and user privacy. The proposed authentication mechanism adopts identity-based encryption scheme and identity-based batch signature scheme to further reduce the total computation cost during one authentication session in comparison with existing authentication schemes. In addition, our scheme eliminates the requirement of storing a lot of pseudo-identities in user's SIM card and supports batch verification feature at the server side to further enhance authentication efficiency. User revocation mechanism is also developed in the proposed scheme for occasions when it is necessary for a home server to revoke individual users. To evaluate security strength of the proposed scheme, the scheme has conducted security analysis through formal proof under Jakobsson et al.’s security model. In comparison with existing authentication schemes, our scheme provides faster authentication along with lower storage consumption in user's SIM card. Hence, the proposed scheme is a very competitive candidate on secure anonymous authentication for mobile roaming services.

Quality of Life and Economic Burden of Respiratory Disease in Asia-Pacific—Asia-Pacific Burden of Respiratory Diseases Study

ObjectivesAsia-Pacific Burden of Respiratory Diseases is a cross-sectional, observational study examining the burden of disease in adults with respiratory diseases across six countries. The aim of this study was to describe health care resource use (HCRU), work impairment, cost burden, and health-related quality of life (HRQOL) associated with respiratory disease in the Asia-Pacific. MethodsConsecutive participants aged 18 years or older with a primary diagnosis of asthma, allergic rhinitis, chronic obstructive pulmonary disease, or rhinosinusitis were enrolled. Participants completed a survey detailing respiratory symptoms, HCRU, work productivity and activity impairment, and HRQOL. Locally sourced unit costs for each country were used in the calculation of total costs. ResultsThe study enrolled 5250 patients. Overall, the mean annual cost for patients with a respiratory disease was US $4191 (SGD 8489) per patient. For patients who reported impairment at work, the mean annual cost was US $7315 (SGD 10,244), with productivity loss being the highest cost component for all four diseases (US $6310 [SGD 9100]). On average, patients were impaired for one-third of their time at work and 5% of their work time missed because of respiratory disease, which resulted in a 36% reduction in productivity. Patients with a primary diagnosis of chronic obstructive pulmonary disease had the greatest impact on HRQOL. ConclusionsIn the Asia-Pacific, respiratory diseases have a significant impact on HCRU and associated costs, along with work productivity. Timely and effective management of these diseases has the potential to reduce disease burden and health care costs and improve work productivity and HRQOL.

Knowledge-Based Resource Allocation for Collaborative Simulation Development in a Multi-Tenant Cloud Computing Environment

Cloud computing technologies have enabled a new paradigm for advanced product development powered by the provision and subscription of computational services in a multi-tenant distributed simulation environment. The description of computational resources and their optimal allocation among tenants with different requirements holds the key to implementing effective software systems for such a paradigm. To address this issue, a systematic framework for monitoring, analyzing and improving system performance is proposed in this research. Specifically, a radial basis function neural network is established to transform simulation tasks with abstract descriptions into specific resource requirements in terms of their quantities and qualities. Additionally, a novel mathematical model is constructed to represent the complex resource allocation process in a multi-tenant computing environment by considering priority-based tenant satisfaction, total computational cost and multi-level load balance. To achieve optimal resource allocation, an improved multi-objective genetic alqorithm is proposed based on the elitist archive and the K -means approaches. As demonstrated in a case study, the proposed framework and methods can effectively support the cloud simulation paradigm and efficiently meet tenants’ computational requirements in a distributed environment.

Active learning surrogate models for the conception of systems with multiple failure modes

Due to the performance and certification criteria, complex mechanical systems have to taken into account several constraints, which can be associated with a series of performance functions. Different software are generally used to evaluate such functions, whose computational cost can vary a lot. In conception or reliability analysis, we thus are interested in the identification of the boundaries of the domain where all these constraints are satisfied, at the minimal total computational cost. To this end, the present work proposes an iterative method to maximize the knowledge about these limits while trying to minimize the required number of evaluations of each performance function. This method is based first on Gaussian process surrogate models that are defined on nested sub-spaces, and second, on an original selection criterion that takes into account the computational cost associated with each performance function. After presenting the theoretical basis of this approach, this paper compares its efficiency to alternative methods on an example.

Entropic lattice Boltzmann simulation of three-dimensional binary gas mixture flow in packed beds using graphics processors

The lattice Boltzmann method is employed for simulating the binary flow of oxygen/nitrogen mixture passing through a highly dense bed of spherical particles. Simulations are performed based on the latest proposed entropic lattice Boltzmann model for multi-component flows, using the D3Q27 lattice stencil. The curved solid boundary of the particles is accurately treated via a linear interpolation. To lower the total computational cost and time of the simulations, implementation on graphics processing units GPU is also presented. Since the workload associated with each iteration is relatively higher than that of conventional 3D LBM simulations, special emphasis is paid in order to obtain the best computational performance on GPUs. Performance gains of one order of magnitude over optimised multi-core CPUs are achieved for the complex flow of interest on Fermi generation GPUs. Moreover, the numerical results for a three-dimensional benchmark flow show excellent agreements with the available analytical data.

Iterative Solution of Boundary Integral Equations for Shallow Water Waves

AbstractThe motion of shallow water, unlike deep water, is influenced by the bottom topology. In order to obtain the velocity potential at any given point of the surface at any given time, we apply boundary integral techniques and solve a system of two integral equations, one expresses the velocity potential at the free surface and on the bottom, the other expresses the streamfunction at the free surface and on the bottom. Boundary integrals can produce very accurate results for shallow water equations but they are too complex to solve analytically and must be solved numerically. In this paper, we use three different numerical methods to compute the solutions to these integral equations: by Gaussian elimination which has high compuational cost; by iteration to construct the Neumann series which converges; and by a preconditioned version of the iteration which effectively lowers the total computation cost.

A Survey of Static Scheduling Algorithm for Distributed Computing System

static scheduling algorithms are widely used to evaluate the performance of distributed computing system. In such systems, purpose of scheduling algorithm is to allocate tasks to available processor so as to efficiently utilize this processor and to reduce the makespan, total computational cost and various other such factors, with the motive of achieving optimal solution. Scheduling algorithms are classified into two broad categories i.e., static scheduling algorithms and dynamic scheduling algorithms. In this paper we are discussing various static scheduling algorithm and numerous problems in various levels of the homogeneous and heterogeneous distributed systems. Also we are comparing these algorithms on the basis of various factors such as speedup, time complexity, scheduling length ratio, normalized scheduling length and so on.

Asymptotically optimal feedback planning using a numerical Hamilton-Jacobi-Bellman solver and an adaptive mesh refinement

We present the first asymptotically optimal feedback planning algorithm for nonholonomic systems and additive cost functionals. Our algorithm is based on three well-established numerical practices: 1) positive coefficient numerical approximations of the Hamilton-Jacobi-Bellman equations; 2) the Fast Marching Method, which is a fast nonlinear solver that utilizes Bellman’s dynamic programming principle for efficient computations; and 3) an adaptive mesh-refinement algorithm designed to improve the resolution of an initial simplicial mesh and reduce the solution numerical error. By refining the discretization mesh globally, we compute a sequence of numerical solutions that converges to the true viscosity solution of the Hamilton-Jacobi-Bellman equations. In order to reduce the total computational cost of the proposed planning algorithm, we find that it is sufficient to refine the discretization within a small region in the vicinity of the optimal trajectory. Numerical experiments confirm our theoretical findings and establish that our algorithm outperforms previous asymptotically optimal planning algorithms, such as PRM* and RRT*.

Pressure Decimation and Interpolation (PDI) method for a baroclinic non-hydrostatic model

Non-hydrostatic models are computationally expensive in simulating density flows and mass transport problems due to the requirement of sufficient grid resolution to resolve density and flow structures. Numerical tests based on the Non-Hydrostatic Wave Model, NHWAVE (Ma et al., 2012), indicated that up to 70% of the total computational cost may be born by the pressure Poisson solver in cases with high grid resolution in both vertical and horizontal directions. However, recent studies using Poisson solver-based non-hydrostatic models have shown that an accurate prediction of wave dispersion does not require a large number of vertical layers if the dynamic pressure is properly discretized. In this study, we explore the possibility that the solution for the dynamic pressure field may, in general, be decimated to a resolution far coarser than that used in representing velocities and other transported quantities, without sacrificing accuracy of solutions. Following van Reeuwijk (2002), we determine the dynamic pressure field by solving the Poisson equation on a coarser grid and then interpolate the pressure field onto a finer grid used for solving for the remaining dynamic variables. With the Pressure Decimation and Interpolation (PDI) method, computational efficiency is greatly improved. We use three test cases to demonstrate the model’s accuracy and efficiency in modeling density flows.

Visual tracking based on extreme learning machine and sparse representation.

The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging learning technique, namely extreme learning machine (ELM). Specifically, visual tracking can be divided into two consecutive processes. Firstly, ELM is utilized to find the optimal separate hyperplane between the target observations and background ones. Thus, the trained ELM classification function is able to remove most of the candidate samples related to background contents efficiently, thereby reducing the total computational cost of the following sparse representation. Secondly, to further combine ELM and sparse representation, the resultant confidence values (i.e., probabilities to be a target) of samples on the ELM classification function are used to construct a new manifold learning constraint term of the sparse representation framework, which tends to achieve robuster results. Moreover, the accelerated proximal gradient method is used for deriving the optimal solution (in matrix form) of the constrained sparse tracking model. Additionally, the matrix form solution allows the candidate samples to be calculated in parallel, thereby leading to a higher efficiency. Experiments demonstrate the effectiveness of the proposed tracker.

A method of probabilistic risk assessment for energy performance and cost using building energy simulation

Energy efficient buildings rely on simulation to predict energy performance. However, problems associated with simulation tools can lead to surprises when discrepancies are found between actual and predicted building energy performance; this frustrates building owners, investors, and designers.A probabilistic method of risk assessment for the calculation of energy use intensity and total utility cost in energy performance has been developed. Sensitive and uncertain parameters were selected and given a probability distribution instead of one fixed value for the simulations. Latin hypercube sampling was used to generate input combinations with parameter values picked stochastically from distributions based on the Monte Carlo method. With these input combinations, 10,000 simulations on seven distributed parameters were run using a cloud processing service. The output data, energy use intensity and energy cost, were analyzed using curve-fitting techniques to find a best-fit distribution, which could be used for risk analysis of energy performance and cost. The results illustrate the probability and reliability of prediction within a specific range. Instead of relying on a single value, these curves would help designers better evaluate design alternatives, and the probability distribution of energy performance and cost would be useful in making decisions about investments for energy efficient projects.

On the Study of the Electromagnetic Scattering by Large Obstacle Based on the Method of Auxiliary Sources

We consider fast and accurate solutions of scattering problems by large perfectly conducting objects (PEC) formulated by an optimization of the Method of Auxiliary Sources (MAS). We present various techniques used to reduce the total computational cost of the scattering problem. The first technique is based on replacing the object by an array of finite number of small (PEC) object with the same shape. The second solution reduces the problem on considering only the half of the object.These two solutions are compared to results from the reference bibliography. Keywords—Method of Auxiliary Sources, Scattering, large object, RCS, computational resources.