Thennarasan Sabapathy
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Preferred name
Thennarasan Sabapathy
Official Name
Thennarasan, Sabapathy
Alternative Name
Sabapathy, Thennarasan
Sabapathy, Thenna
Sabapathy, T.
Sapabathy, T.
Main Affiliation
Scopus Author ID
35424377200
Researcher ID
AAA-9706-2019

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  • Publication
    A novel unsupervised spectral clustering for pure-tone audiograms towards hearing aid filter bank design and initial configurations
    ( 2022-01-01)
    Elkhouly A.
    ;
    Allan Melvin Andrew
    ;
    Hasliza A Rahim @ Samsuddin
    ;
    Abdulaziz N.
    ;
    Abdulmalek M.
    ;
    Mohd Najib Mohd Yasin
    ;
    Muzammil Jusoh
    ;
    Thennarasan Sabapathy
    ;
    Siddique S.
    The current practice of adjusting hearing aids (HA) is tiring and time-consuming for both patients and audiologists. Of hearing-impaired people, 40–50% are not satisfied with their HAs. In addition, good designs of HAs are often avoided since the process of fitting them is exhausting. To improve the fitting process, a machine learning (ML) unsupervised approach is proposed to cluster the pure-tone audiograms (PTA). This work applies the spectral clustering (SP) approach to group audiograms according to their similarity in shape. Different SP approaches are tested for best results and these approaches were evaluated by Silhouette, Calinski-Harabasz, and Davies-Bouldin criteria values. Kutools for Excel add-in is used to generate audiograms’ population, annotated using the results from SP, and different criteria values are used to evaluate population clusters. Finally, these clusters are mapped to a standard set of audiograms used in HA characterization. The results indicated that grouping the data in 8 groups or 10 results in ones with high evaluation criteria. The evaluation for population audiograms clusters shows good performance, as it resulted in a Silhouette coefficient >0.5. This work introduces a new concept to classify audiograms using an ML algorithm according to the audiograms’ similarity in shape.
  • Publication
    Junction engineering in two-stepped recessed SiGe MOSFETs for high performance application
    ( 2020-01-08)
    Wahab Y.A.
    ;
    Soin N.
    ;
    Naseer M.N.
    ;
    Hussin H.
    ;
    Rozana Aina Maulat Osman
    ;
    Johan M.R.
    ;
    Hamizi N.A.
    ;
    Pivehzhani O.A.
    ;
    Chowdhury Z.Z.
    ;
    Sagadevan S.
    ;
    Thennarasan Sabapathy
    SiGe being one of the most diverse, emerging and richest material for MOSFETs is alluring the scientist due to its unique characteristics like high performance, high surface channel mobility and low threshold voltage as compare to that of silicon MOSFETs. In spite of so much cruciality, the performance of SiGe MOSFETs is being controlled by several factors like resistance and junction depth. These factors urged scientists to not relieve on the traditional methods of junction meteorology to utilize the actual potential and high performance of SiGe MOSFETs in terms of their application. Hence, a paradigm shift in junction engineering is being observed during last few years and discussed. The main focus of this paper is to highlight junction metrologies (ion implantation and annealing) that were tested on SiGe MOSFETs and to propose the most efficient and sustainable technique of junction engineering for high performance applications.
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