What is he best known for?
The fields of study he is best known for:
- Algorithm
- Artificial intelligence
- Statistics
His scientific interests lie mostly in Parallel computing, Algorithm, Very-large-scale integration, Decoding methods and Electronic engineering.
His work deals with themes such as Fast Fourier transform and Latency, which intersect with Parallel computing.
His research in Algorithm intersects with topics in Digital filter, Upper and lower bounds, Wavelet transform and Filter design.
His Digital filter study integrates concerns from other disciplines, such as Theoretical computer science, Adaptive filter, Interleaving, Signal processing and Finite impulse response.
His research integrates issues of Parallel algorithm, Algorithm design, Cryptography and Finite field in his study of Very-large-scale integration.
His biological study spans a wide range of topics, including Round-off error, Multiplexer, Adder and Equalization.
His most cited work include:
- Vlsi Digital Signal Processing Systems: Design And Implementation (1029 citations)
- High-speed VLSI architectures for the AES algorithm (357 citations)
- VLSI architectures for discrete wavelet transforms (357 citations)
What are the main themes of his work throughout his whole career to date?
Keshab K. Parhi mostly deals with Algorithm, Parallel computing, Electronic engineering, Decoding methods and Very-large-scale integration.
In his study, Filter is strongly linked to Digital filter, which falls under the umbrella field of Algorithm.
His Parallel computing study incorporates themes from Digital signal processing and Adder.
Digital signal processing is a subfield of Computer hardware that Keshab K. Parhi explores.
His Electronic engineering research focuses on Finite impulse response and CMOS.
His research investigates the connection with Turbo code and areas like Serial concatenated convolutional codes which intersect with concerns in Error floor.
He most often published in these fields:
- Algorithm (27.47%)
- Parallel computing (25.78%)
- Electronic engineering (17.32%)
What were the highlights of his more recent work (between 2014-2021)?
- Algorithm (27.47%)
- Artificial intelligence (9.11%)
- Pattern recognition (7.16%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Algorithm, Artificial intelligence, Pattern recognition, Artificial neural network and Computation.
His Algorithm research includes elements of Electronic circuit, Theoretical computer science and Coding.
The concepts of his Artificial intelligence study are interwoven with issues in Graph and Computer vision.
His study in the fields of Support vector machine, Feature selection and Classifier under the domain of Pattern recognition overlaps with other disciplines such as Major depressive disorder.
Computation connects with themes related to Fast Fourier transform in his study.
He interconnects Algorithm design and Communication channel in the investigation of issues within Decoding methods.
Between 2014 and 2021, his most popular works were:
- Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Subimage Classification (177 citations)
- Iterative Vessel Segmentation of Fundus Images (147 citations)
- Low-Latency Successive-Cancellation List Decoders for Polar Codes With Multibit Decision (99 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Artificial intelligence
- Statistics
His main research concerns Artificial intelligence, Pattern recognition, Algorithm, Segmentation and Image segmentation.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Infomax, Blind signal separation and Group.
The study incorporates disciplines such as False positive rate and Human Connectome Project in addition to Pattern recognition.
His Algorithm research is multidisciplinary, incorporating perspectives in Digital filter, Theoretical computer science, Perceptron and Trigonometric functions.
His work in Fast Fourier transform addresses subjects such as Overhead, which are connected to disciplines such as Digital signal processing.
His study in Decoding methods is interdisciplinary in nature, drawing from both Sorting, Very-large-scale integration and Critical path method.
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