Search Results - (( java application scheduling algorithm ) OR ( data interpolation data algorithm ))

Numerical Integration And Data Reduction For Application In Photometry by Chew, Tong Fatt

“…In this thesis, an algorithm based on a local interpolation scheme called the Average Angle Method was developed for numerical integration and data reduction of photometric data, and in general, for any data set sharing similar characteristics as the luminous intensity data The new interpolation scheme interpolates discrete data with piecewise cubic polynomials having a CI continuity in the interior. knots. …”
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Optimal search algorithm in a big database using interpolation–extrapolation method by Kabir, M. Nomani, Alginahi, Yasser M., Ali, J., Abdel-Raheem, E.

“…Search using traditional binary-search algorithm can be accelerated by employing an interpolation search technique when the data is regularly distributed. …”
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Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

“…The CPU profiler of JavaTM VisualVM measures the number of invocations of scheduling event handlers (procedures) in each algorithm as well as the total time spent in all invocations of this handler. …”
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Exponential Parameterized Cubic B-Spline Curves And Surfaces by Ammad, Muhammad

“…In developing B-spline curve interpolation method, an algorithm is presented for interpolating data points. …”
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Analysis of multiexponential transient signals using interpolation-based deconvolution and parametric modeling techniques by Salami, Momoh Jimoh Eyiomika, Ismail, Z.

“…One method of overcoming this d1ficulty is by incorporating the spline interpolation algorithm into the nonlinear preprocessing procedure. …”
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A Toolkit for Simulation of Desktop Grid Environment by FOROUSHAN, PAYAM CHINI

“…In this type of environment it is nearly impossible to prove the effectiveness of a scheduling algorithm. Hence the main objective of this study is to develop a desktop grid simulator toolkit for measuring and modeling scheduler algorithm performance. …”
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Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT by Abdullah, Amran

“…Genetic Algorithm is one of the most popular optimization solutions used in various applications such as scheduling. …”
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STEP-NC Interpolator for General 2D and 3D Parametric Curve / Dzullijah Ibrahim...[et al.] by Ibrahim, Dzullijah, Yaakob, Yusli, Hussin, Norasikin, Samad, Zahurin

“…A STEP-NC interface to OAPC-NC interpolator using STEP-NC tool path data is proposed and developed. …”
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Development of android application for peninsular Malaysia GEOID height calculator / Mohamad Nazir Md Nazri by Md Nazri, Mohamad Nazir

“…Verification of algorithm of interpolation method had verified that only two interpolation methods are useable for producing results which are Bilinear Interpolation and Inverse Distance Weighted. …”
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Improving Class Timetabling using Genetic Algorithm by Qutishat, Ahmed Mohammed Ali

“…This paper reports the power fill techniques using GA in scheduling. Class timetabling problem is one of the applications in scheduling. …”
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Examination timetabling using genetic algorithm case study: KUiTTHO by Mohd Salikon, Mohd Zaki

“…This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. …”
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Interpolation and extrapolation techniques based Neural Network in estimating the missing ionospheric TEC data by Jayapal V., Zain A.F.M.

“…Normalized RMSE, RMSE and relative correction are computed for both methods to evaluate the capability of NN to interpolate and extrapolate the missing data. The results show that NN is able to interpolate the missing TEC data within the input range better than extrapolating the missing data outside the input range. …”

Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO by Mohd. Zaki, Mohd. Salikon

“…This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. …”
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Interpolation and Extrapolation Techniques Based Neural Network in Estimating the Missing Ionospheric TEC Data by Jayapal, V, Zain, AFM

An efficient method of biometric matching using interpolated ECG data by Sidek, Khairul Azami, Sufi, Fahim, Khalil, Ibrahim, Al-Shammary, Dhiah

“…Results of experiments confirmed that the template matching using interpolation method achieved better accuracy (up to 4.46%) than the existing method without interpolation when using ECG data with lower sampling rate.…”
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

“…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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THE DEVELOPMENT OF C1 CUBIC TIMMER TRIANGULAR PATCHES FOR SCATTERED DATA INTERPOLATION by MOHD ALI, FATIN AMANI

“…Finally, the cubic Timmer triangular patch is used to reconstruct a surface from non-uniform and uniform scattered data. To do this, we employ three stages algorithm…”
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Comparison of envelope interpolation techniques in Empirical Mode Decomposition (EMD) for eyeblink artifact removal from EEG by Egambaram, A., Badruddin, N., Asirvadam, V.S., Begum, T.

“…However, the algorithm is relatively slow for real time processing due to the iterative nature of EMD and the fact that interpolation of large number of data points consumes a lot of computer resources. …”
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Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

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Trigonometric-Euclidean-Smoother Interpolator (TESI) for continuous time-series and non-time-series data augmentation for deep neural network applications in agriculture by Derraz, Radhwane, Muharam, Farrah Melissa, Jaafar, Noraini Ahmad, Yap, Ng Keng

“…Deep neural networks (DNNs) can predict such crop variables. However, DNNs are data greedy algorithms that overfit/underfit on small-size datasets. …”
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