Search Results - Novel Methods for 3D Data Compression with Comparative Analysis via the Fourier Transform

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  1. 1

    A Comparative Analysis of the Lossless Data Compression Methods for Unsparsed Tabular Data by Erkus, Ekin Can, Bursali, Ahmet

    Published: IEEE 25.07.2024
    “…This paper conducts a comparative analysis of the impact of unsparsing by data scaling on lossless data compression methods…”
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    Conference Proceeding
  2. 2

    Comparative Analysis of K-RLE and LTC Methods for Measurement Data Compression with Losses Control by Semenov, Konstantin, Raimzhanova, Adele

    Published: IEEE 28.05.2025
    “…The requirements imposed by metrology on measurement results restrict the feasibility of their archiving when large data volumes are involved - notably if one uses lossy compression methods…”
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    Conference Proceeding
  3. 3

    Comparative Analysis of Statistical Test Based on Data Compression Methods and Standard Tests for Assessing Randomness of Random Number Generators by Lulu, Yeshewas Getachew

    ISSN: 2995-0996
    Published: IEEE 30.09.2024
    “…This paper presents a detailed comparative analysis of statistical tests utilizing both modern data compressors and standard statistical methods for assessing the randomness of Ran-dom number generators(RNG…”
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    Conference Proceeding
  4. 4

    A novel smart meter data compression method via stacked convolutional sparse auto-encoder by Wang, Shouxiang, Chen, Haiwen, Wu, Lei, Wang, Jianfeng

    ISSN: 0142-0615
    Published: Elsevier Ltd 01.06.2020
    “… In this paper, a deep-learning-based compression method for smart meter data is proposed via stacked convolutional sparse auto-encoder (SCSAE…”
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    Journal Article
  5. 5

    Comparative Analysis of Deep Argo in Situ Data With a Novel Extrapolation Method & Illustration of Anomalies by Piao, Shengchun, Iqbal, Kashif, Zhang, Minghui, Nansong, Li

    Published: IEEE 05.10.2020
    “… The need for this particular activity was compelled by requirement of weather-related ocean data desired by the oceanographic researchers around the world…”
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    Conference Proceeding
  6. 6

    A novel, objective, quantitative method of evaluation of the back pain component using comparative computerized multi-parametric tactile mapping before/after spinal cord stimulation and database analysis: The “Neuro-Pain’t” software by Rigoard, P., Nivole, K., Blouin, P., Monlezun, O., Roulaud, M., Lorgeoux, B., Bataille, B., Guetarni, F.

    ISSN: 0028-3770, 1773-0619, 1773-0619
    Published: France Elsevier Masson SAS 01.03.2015
    Published in Neuro-chirurgie (01.03.2015)
    “…One of the major challenges of neurostimulation is actually to address the back pain component in patients suffering from refractory chronic back and leg pain…”
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    Journal Article
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  9. 9

    On the performance of surface electromyography-based onset detection methods with real data in assistive technologies: Comparative analysis and enhancements via sensor fusion by Reis, Margarida, Almeida, Carlos, Rocha, Rui M.

    ISSN: 1380-7501, 1573-7721
    Published: New York Springer US 01.05.2018
    Published in Multimedia tools and applications (01.05.2018)
    “… In the literature, various methods to determine this onset have been studied, but mainly for the non-disabled population and may not be designed to deal with the low signal-to-noise ratio, motion…”
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    Journal Article
  10. 10

    A Novel, Objective, Quantitative Method of Evaluation of the Pain Component and Paresthesia Coverage using Comparative Computerized Multiparametric Tactile Mapping and Database Analysis: The “Neuro-Mapping-Tools” Software (N3MT) by Roulaud, Manuel, Guetarni, Farid, Nivole, Kévin, Monlezun, Olivier, Lorgeoux, Bertille, Rigoard, Philippe

    ISSN: 2192-5682, 2192-5690
    Published: Los Angeles, CA SAGE Publications 01.04.2016
    Published in Global spine journal (01.04.2016)
    “… and neurostimulation outcomes for those who undergo neurostimulation implantation. Material and Methods Our neuroinformatics laboratory (N3Laboratory…”
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    Journal Article
  11. 11

    Optimizing Parkinson’s Disease Prediction: A Comparative Analysis of Data Aggregation Methods Using Multiple Voice Recordings via an Automated Artificial Intelligence Pipeline by Yang, Zhengxiao, Zhou, Hao, Srivastav, Sudesh, Shaffer, Jeffrey G., Abraham, Kuukua E., Naandam, Samuel M., Kakraba, Samuel

    ISSN: 2306-5729, 2306-5729
    Published: Basel MDPI AG 01.01.2025
    Published in Data (Basel) (01.01.2025)
    “… This study compares four data aggregation methods designed to tackle the grouped structure in classification tasks…”
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    Journal Article
  12. 12

    Stability analysis of sampled-data systems via novel Lyapunov functional method by Sheng, Zhaoliang, Lin, Chong, Chen, Bing, Wang, Qing-Guo

    ISSN: 0020-0255, 1872-6291
    Published: Elsevier Inc 01.03.2022
    Published in Information sciences (01.03.2022)
    “…This study focuses on the stability analysis of sampled-data systems. The idea is to propose a novel Lyapunov functional method using a new framework…”
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    Journal Article
  13. 13

    A Novel Classification Method for Analysis of Multi-stage Diseases via Mass Spectrometric Data by Oh, Jung Hun, Kim, Young Bun, Gao, Jean

    ISBN: 0769530311, 9780769530314
    Published: IEEE 01.01.2007
    “…Multi-category classification is one of the challenging issues in medical data analysis…”
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    Conference Proceeding
  14. 14

    Novel feature selection method via kernel tensor decomposition for improved multi-omics data analysis by Y-H, Taguchi, Turki, Turki

    ISSN: 2692-8205, 2692-8205
    Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 17.12.2021
    Published in bioRxiv (17.12.2021)
    “…Background: Feature selection of multi-omics data analysis remains challenging owing to the size of omics datasets, comprising approximately 102-105 features…”
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    Paper
  15. 15

    Novel feature selection method via kernel tensor decomposition for improved multi-omics data analysis by Taguchi, Y-h., Turki, Turki

    ISSN: 1755-8794, 1755-8794
    Published: London BioMed Central 24.02.2022
    Published in BMC medical genomics (24.02.2022)
    “…Background Feature selection of multi-omics data analysis remains challenging owing to the size of omics datasets, comprising approximately 10 2 – 10 5 features…”
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    Journal Article
  16. 16