ASWANI KUMAR UNNAM

@nagarjunauniversity.ac.in

Research Scholar, Department of Computer Science and Engineering
acharya nagarjuna university

ASWANI KUMAR UNNAM


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https://researchid.co/askphy09

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Computer Science, Computer Science Applications, Information Systems
5

Scopus Publications

Scopus Publications

  • LINEAR SUBSPAC31E LEARNING-BASED CLUSTER TENDENCY ASSESSMENT VISUAL MODELS FOR HIGH-DIMENSIONAL BIG DATASETS
    Journal of Theoretical and Applied Information Technology, 2024
  • LINEAR SUBSPACE LEARNING-BASED CLUSTER TENDENCY ASSESSMENT VISUAL MODELS FOR HIGH-DIMENSIONAL BIG DATASETS
    Journal of Theoretical and Applied Information Technology, 2024
  • Pre-Clustering Tendency Visual Assessment of Techniques for Effective Data Partitions
    15th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2024, 2024
  • An 81.5dB SNDR, 2.5 MHz Bandwidth Incremental Continuous-Time Delta-Sigma ADC in 180 nm CMOS
    Aswani Kumar Unnam, Paramita Banerjee, Nagendra Krishnapura
    IEEE Solid State Circuits Letters, 2024
    Adapting a continuous time delta-sigma analog-to-digital converter (ADC) for incremental operation at high sampling rates degrades the noise and distortion due to potential overload of the modulator as it comes out of reset and nonlinear residue on the reset switch due to input current flowing through it in the reset phase. It is shown that the input and DAC currents must simultaneously begin to flow through the first integrating capacitor to minimize the possibility of overload. The first integrator reset has to be released just before the start of the DAC pulse. A feedforward path must be used to ensure that the DAC output is close to the input signal from the beginning. Blocking the input current from flowing through the reset switch in the reset phase eliminates the effect of the nonlinear residue. A 320 MS/s fourth-order incremental delta-sigma ADC prototype in an 180nm process using the above techniques has 90 dB dynamic range, 82 dB SNDR, and 84.5 dB SNR in a 2.5 MHz bandwidth. It consumes 46.3 mW from a 1.8V supply and occupies 0.7 mm2.
  • An Extended Clusters Assessment Method with the Multi-Viewpoints for Effective Visualization of Data Partitions
    International Journal of Intelligent Systems and Applications in Engineering, 2023

RECENT SCHOLAR PUBLICATIONS

  • LINEAR SUBSPAC31E LEARNING-BASED CLUSTER TENDENCY ASSESSMENT VISUAL MODELS FOR HIGH-DIMENSIONAL BIG DATASETS
    AK UNNAM, DRBS RAO
    Journal of Theoretical and Applied Information Technology 102 (10) , 2024
    2024
  • An Extended Clusters Assessment Method with the Multi-Viewpoints for Effective Visualization of Data Partitions
    AK Unnam, BS Rao
    International Journal of Intelligent Systems and Applications in Engineering … , 2023
    2023
  • An Enhanced Sampling-Based Viewpoints Cosine Visual Model for an Efficient Big Data Clustering
    DBSR Aswani Kumar Unnam
    International Journal on Recent and Innovation Trends in Computing and … , 2023
    2023

MOST CITED SCHOLAR PUBLICATIONS

  • LINEAR SUBSPAC31E LEARNING-BASED CLUSTER TENDENCY ASSESSMENT VISUAL MODELS FOR HIGH-DIMENSIONAL BIG DATASETS
    AK UNNAM, DRBS RAO
    Journal of Theoretical and Applied Information Technology 102 (10) , 2024
    2024
  • An Extended Clusters Assessment Method with the Multi-Viewpoints for Effective Visualization of Data Partitions
    AK Unnam, BS Rao
    International Journal of Intelligent Systems and Applications in Engineering … , 2023
    2023
  • An Enhanced Sampling-Based Viewpoints Cosine Visual Model for an Efficient Big Data Clustering
    DBSR Aswani Kumar Unnam
    International Journal on Recent and Innovation Trends in Computing and … , 2023
    2023