Search Results - (( programming based maps algorithm ) OR ( java application learning algorithm ))

Refine Results
  1. 1

    Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform by Alias, Norma, Satam, Noriza, Abd. Ghaffar, Zarith Safiza, Darwis, Roziha, Hamzah, Norhafiza, Islam, Md. Rajibul

    Published 2009
    “…The development of this architecture is based on several programming language as it involves algorithm implementation on C, parallelization using Parallel Virtual Machine (PVM) and Java for web services development. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  2. 2
  3. 3
  4. 4

    Development of floor mapping mobile robot algorithm using enhanced Artificial Neuro-Based SLAM (ANBS) by Khairul Anuar Juhari, Rizauddin Ramli, Sallehuddin Mohamed Haris, Zunaidi Ibrahim, Abdullah Zawawi Mohamed

    Published 2020
    “…The paper will highlight implementing a Rplidar sensor with a floor mapping mobile robot platform with the enhanced error corrections based on the Artificial Neuro-Based SLAM (ANBS) algorithm. …”
    Get full text
    Get full text
    Get full text
    Article
  5. 5

    Designing of disparity map based on hierarchical dynamic programming using satellite stereo imagery by Qayyum, A., Malik, A.S., Naufal, M., Saad, M., Mazher, M., Rasheed, W., Abdullah, T.A.R.B.T.

    Published 2016
    “…The 3D depth of vegetation is based on disparity map which has been measured using stereo algorithms. …”
    Get full text
    Get full text
    Conference or Workshop Item
  6. 6
  7. 7
  8. 8

    Evaluation of dynamic programming among the existing stereo matching algorithms by Teo, Chee Huat, Nurulfajar, Abd Manap

    Published 2015
    “…The dynamic programming algorithm used on this research is the current method as its disparity estimates at a particular pixel and all the other pixels unlike the old methods which with scanline based of dynamic programming. …”
    Get full text
    Get full text
    Get full text
    Article
  9. 9

    Optimizing Visual Sensors Placement with Risk Maps Using Dynamic Programming by Altahir, A.A., Asirvadam, V.S., Sebastian, P., Hamid, N.H.B., Ahmed, E.F.

    Published 2022
    “…That is, the enhanced efficiency of coverage is obtained by developing a prior grid assessment practice to stress on the security sensitive zones. Then, the dynamic programming algorithm operates on security quantified maps rather than uniform grids. …”
    Get full text
    Get full text
    Article
  10. 10

    An Educational Tool Aimed at Learning Metaheuristics by Kader, Md. Abdul, Jamaluddin, Jamal A., Kamal Z., Zamli

    Published 2020
    “…In this paper, we introduce an education tool for learning metaheuristic algorithms that allows displaying the convergence speed of the corresponding metaheuristic upon setting/changing the dependable parameters. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  11. 11

    A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment by Ruzita, Ahmad

    Published 2013
    “…The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
    Get full text
    Get full text
    Get full text
    Thesis
  12. 12

    Enhanced Image View Synthesis Using Multistage Hybrid Median Filter For Stereo Images by Ali Hussein Aboali, Maged Yahya

    Published 2018
    “…Disparity depth map estimation of stereo matching algorithm is one of the most active research topics in computer vision.In the field of image processing,many existing stereo matching algorithms to obtain disparity depth map are developed and designed with low accuracy.To improve the accuracy of disparity depth map is quite challenging and difficult especially with uncontrolled dynamic environment.The accuracy is affected by many unwanted aspects including random noises,horizontal streaks,low texture,depth map non-edge preserving, occlusion,and depth discontinuities.Thus,this research proposed a new robust method of hybrid stereo matching algorithm with significant accuracy of computation.The thesis will present in detail the development,design, and analysis of performance on Multistage Hybrid Median Filter (MHMF).There are two main parts involved in our developed method which combined in two main stages.Stage 1 consists of the Sum of Absolute Differences (SAD) from Basic Block Matching (BBM) algorithm and the part of Scanline Optimization (SO) from Dynamic Programming (DP) algorithm.While,Stage 2 is the main core of our MHMF as a post-processing step which included segmentation,merging, and hybrid median filtering.The significant feature of the post-processing step is on its ability to handle efficiently the unwanted aspects obtained from the raw disparity depth map on the step of optimization.In order to remove and overcome the challenges unwanted aspects, the proposed MHMF has three stages of filtering process along with the developed approaches in Stage 2 of MHMF algorithm.There are two categories of evaluation performed on the obtained disparity depth map: subjective evaluation and objective evaluation.The objective evaluation involves the evaluation on Middlebury Stereo Vision system and evaluation using traditional methods such as Mean Square Errors (MSE),Peak to Signal Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM).Based on the results of the standard benchmarking datasets from Middlebury,the proposed algorithm is able to reduce errors of non-occluded and all errors respectively.While,the subjective evaluation is done for datasets captured from MV BLUE FOX camera using human's eyes perception.Based on the results,the proposed MHMF is able to obtain accurate results, specifically 69% and 71% of non-occluded and all errors for disparity depth map, and it outperformed some of the existing methods in the literature such as BBM and DP algorithms.…”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
  13. 13
  14. 14

    Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

    Published 2022
    “…This thesis is based on the application of sentiment classification algorithm to tweet data with the goal of classifying messages based on the polarity of sentiment towards a particular topic (or subject matter). …”
    Get full text
    Get full text
    Get full text
    Thesis
  15. 15

    Off-the-shelf indoor localization system using radio frequency for wireless local area network by Alhammadi, Abdulraqeb Shaif Ahmed

    Published 2018
    “…In the online phase, the proposed model infers the unknown locations based on the RPs available in the radio map. The user location is inferred based on three dimensional (3-D) Bayesian graphical model using the OpenBUGS program. …”
    Get full text
    Get full text
    Thesis
  16. 16

    Development Of Double Stage Filter (DSF) On Stereo Matching Algorithm For 3D Computer Vision Applications by Teo, Chee Huat

    Published 2016
    “…Based on the results of evaluations, the results obtained by DSF is better than the algorithms, basic block matching and dynamic programming.…”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
  17. 17

    Very Large Scale Integration Cell Based Path Extractor For Physical To Layout Mapping In Fault Isolation Work by Pragasam, Matthew

    Published 2017
    “…Therefore the path extractor program proposed incorporates the characteristics of a depth-first search algorithm by considering the specifications of a cell-based design. …”
    Get full text
    Get full text
    Thesis
  18. 18
  19. 19
  20. 20